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Data Mining Classification and Prediction Presentation Transcript
1.Data Mining: Concepts and Techniques
2.Classification and Prediction
What is classification? What is prediction?
Issues regarding classification and prediction
Classification by decision tree induction
Bayesian Classification
Classification by backpropagation
Classification based on concepts from association rule mining
Other Classification Methods
Prediction
Classification accuracy
Summary
What is classification? What is prediction?
Issues regarding classification and prediction
Classification by decision tree induction
Bayesian Classification
Classification by backpropagation
Classification based on concepts from association rule mining
Other Classification Methods
Prediction
Classification accuracy
Summary
3.Classification vs. Prediction
Classification:
predicts categorical class labels
classifies data (constructs a model) based on the training set and the values (class labels) in a classifying attribute and uses it in classifying new data
Prediction:
models continuous-valued functions, i.e., predicts unknown or missing values
Typical Applications
credit approval
target marketing
medical diagnosis
treatment effectiveness analysis
Classification:
predicts categorical class labels
classifies data (constructs a model) based on the training set and the values (class labels) in a classifying attribute and uses it in classifying new data
Prediction:
models continuous-valued functions, i.e., predicts unknown or missing values
Typical Applications
credit approval
target marketing
medical diagnosis
treatment effectiveness analysis
4.Classification—A Two-Step Process
Model construction: describing a set of predetermined classes
Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute
The set of tuples used for model construction: training set
The model is represented as classification rules, decision trees, or mathematical formulae
Model usage: for classifying future or unknown objects
Estimate accuracy of the model
The known label of test sample is compared with the classified result from the model
Accuracy rate is the percentage of test set samples that are correctly classified by the model
Test set is independent of training set, otherwise over-fitting will occur
Model construction: describing a set of predetermined classes
Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute
The set of tuples used for model construction: training set
The model is represented as classification rules, decision trees, or mathematical formulae
Model usage: for classifying future or unknown objects
Estimate accuracy of the model
The known label of test sample is compared with the classified result from the model
Accuracy rate is the percentage of test set samples that are correctly classified by the model
Test set is independent of training set, otherwise over-fitting will occur
5.Classification Process (1): Model Construction
6.Classification Process (2): Use the Model in Prediction
7.Supervised vs. Unsupervised Learning
Supervised learning (classification)
Supervision: The training data (observations, measurements, etc.) are accompanied by labels indicating the class of the observations
New data is classified based on the training set
Unsupervised learning (clustering)
The class labels of training data is unknown
Given a set of measurements, observations, etc. with the aim of establishing the existence of classes or clusters in the data
Supervised learning (classification)
Supervision: The training data (observations, measurements, etc.) are accompanied by labels indicating the class of the observations
New data is classified based on the training set
Unsupervised learning (clustering)
The class labels of training data is unknown
Given a set of measurements, observations, etc. with the aim of establishing the existence of classes or clusters in the data
8.Classification and Prediction
What is classification? What is prediction?
Issues regarding classification and prediction
Classification by decision tree induction
Bayesian Classification
Classification by backpropagation
Classification based on concepts from association rule mining
Other Classification Methods
Prediction
Classification accuracy
Summary
What is classification? What is prediction?
Issues regarding classification and prediction
Classification by decision tree induction
Bayesian Classification
Classification by backpropagation
Classification based on concepts from association rule mining
Other Classification Methods
Prediction
Classification accuracy
Summary
9.Issues regarding classification and prediction (1): Data Preparation
Data cleaning
Preprocess data in order to reduce noise and handle missing values
Relevance analysis (feature selection)
Remove the irrelevant or redundant attributes
Data transformation
Generalize and/or normalize data
Data cleaning
Preprocess data in order to reduce noise and handle missing values
Relevance analysis (feature selection)
Remove the irrelevant or redundant attributes
Data transformation
Generalize and/or normalize data
10.Issues regarding classification and prediction (2): Evaluating Classification Methods
Predictive accuracy
Speed and scalability
time to construct the model
time to use the model
Robustness
handling noise and missing values
Scalability
efficiency in disk-resident databases
Interpretability:
understanding and insight provded by the model
Goodness of rules
decision tree size
compactness of classification rules
Predictive accuracy
Speed and scalability
time to construct the model
time to use the model
Robustness
handling noise and missing values
Scalability
efficiency in disk-resident databases
Interpretability:
understanding and insight provded by the model
Goodness of rules
decision tree size
compactness of classification rules
11.Classification and Prediction
What is classification? What is prediction?
Issues regarding classification and prediction
Classification by decision tree induction
Bayesian Classification
Classification by backpropagation
Classification based on concepts from association rule mining
Other Classification Methods
Prediction
Classification accuracy
Summary
What is classification? What is prediction?
Issues regarding classification and prediction
Classification by decision tree induction
Bayesian Classification
Classification by backpropagation
Classification based on concepts from association rule mining
Other Classification Methods
Prediction
Classification accuracy
Summary
12.Classification by Decision Tree Induction
Decision tree
A flow-chart-like tree structure
Internal node denotes a test on an attribute
Branch represents an outcome of the test
Leaf nodes represent class labels or class distribution
Decision tree generation consists of two phases
Tree construction
At start, all the training examples are at the root
Partition examples recursively based on selected attributes
Tree pruning
Identify and remove branches that reflect noise or outliers
Use of decision tree: Classifying an unknown sample
Test the attribute values of the sample against the decision tree
Decision tree
A flow-chart-like tree structure
Internal node denotes a test on an attribute
Branch represents an outcome of the test
Leaf nodes represent class labels or class distribution
Decision tree generation consists of two phases
Tree construction
At start, all the training examples are at the root
Partition examples recursively based on selected attributes
Tree pruning
Identify and remove branches that reflect noise or outliers
Use of decision tree: Classifying an unknown sample
Test the attribute values of the sample against the decision tree
13.Training Dataset
14.Output: A Decision Tree for “buys_computer”
15.Algorithm for Decision Tree Induction
Basic algorithm (a greedy algorithm)
Tree is constructed in a top-down recursive divide-and-conquer manner
At start, all the training examples are at the root
Attributes are categorical (if continuous-valued, they are discretized in advance)
Examples are partitioned recursively based on selected attributes
Test attributes are selected on the basis of a heuristic or statistical measure (e.g., information gain)
Conditions for stopping partitioning
All samples for a given node belong to the same class
There are no remaining attributes for further partitioning – majority voting is employed for classifying the leaf
There are no samples left
Basic algorithm (a greedy algorithm)
Tree is constructed in a top-down recursive divide-and-conquer manner
At start, all the training examples are at the root
Attributes are categorical (if continuous-valued, they are discretized in advance)
Examples are partitioned recursively based on selected attributes
Test attributes are selected on the basis of a heuristic or statistical measure (e.g., information gain)
Conditions for stopping partitioning
All samples for a given node belong to the same class
There are no remaining attributes for further partitioning – majority voting is employed for classifying the leaf
There are no samples left
16.Attribute Selection Measure
Information gain (ID3/C4.5)
All attributes are assumed to be categorical
Can be modified for continuous-valued attributes
Gini index (IBM IntelligentMiner)
All attributes are assumed continuous-valued
Assume there exist several possible split values for each attribute
May need other tools, such as clustering, to get the possible split values
Can be modified for categorical attributes
Information gain (ID3/C4.5)
All attributes are assumed to be categorical
Can be modified for continuous-valued attributes
Gini index (IBM IntelligentMiner)
All attributes are assumed continuous-valued
Assume there exist several possible split values for each attribute
May need other tools, such as clustering, to get the possible split values
Can be modified for categorical attributes
17.Information Gain (ID3/C4.5)
Select the attribute with the highest information gain
Assume there are two classes, P and N
Let the set of examples S contain p elements of class P and n elements of class N
Select the attribute with the highest information gain
Assume there are two classes, P and N
Let the set of examples S contain p elements of class P and n elements of class N
18.Information Gain in Decision Tree Induction
19.Attribute Selection by Information Gain Computation
20.Gini Index (IBM IntelligentMiner)
21.Extracting Classification Rules from Trees
22.Avoid Overfitting in Classification
The generated tree may overfit the training data
Too many branches, some may reflect anomalies due to noise or outliers
Result is in poor accuracy for unseen samples
Two approaches to avoid overfitting
Prepruning: Halt tree construction early—do not split a node if this would result in the goodness measure falling below a threshold
Difficult to choose an appropriate threshold
Postpruning: Remove branches from a “fully grown” tree—get a sequence of progressively pruned trees
Use a set of data different from the training data to decide which is the “best pruned tree”
The generated tree may overfit the training data
Too many branches, some may reflect anomalies due to noise or outliers
Result is in poor accuracy for unseen samples
Two approaches to avoid overfitting
Prepruning: Halt tree construction early—do not split a node if this would result in the goodness measure falling below a threshold
Difficult to choose an appropriate threshold
Postpruning: Remove branches from a “fully grown” tree—get a sequence of progressively pruned trees
Use a set of data different from the training data to decide which is the “best pruned tree”
23.Approaches to Determine the Final Tree Size
Separate training (2/3) and testing (1/3) sets
Use cross validation, e.g., 10-fold cross validation
Use all the data for training
but apply a statistical test (e.g., chi-square) to estimate whether expanding or pruning a node may improve the entire distribution
Use minimum description length (MDL) principle:
halting growth of the tree when the encoding is minimized
Separate training (2/3) and testing (1/3) sets
Use cross validation, e.g., 10-fold cross validation
Use all the data for training
but apply a statistical test (e.g., chi-square) to estimate whether expanding or pruning a node may improve the entire distribution
Use minimum description length (MDL) principle:
halting growth of the tree when the encoding is minimized
24.Enhancements to basic decision tree induction
Allow for continuous-valued attributes
Dynamically define new discrete-valued attributes that partition the continuous attribute value into a discrete set of intervals
Handle missing attribute values
Assign the most common value of the attribute
Assign probability to each of the possible values
Attribute construction
Create new attributes based on existing ones that are sparsely represented
This reduces fragmentation, repetition, and replication
Allow for continuous-valued attributes
Dynamically define new discrete-valued attributes that partition the continuous attribute value into a discrete set of intervals
Handle missing attribute values
Assign the most common value of the attribute
Assign probability to each of the possible values
Attribute construction
Create new attributes based on existing ones that are sparsely represented
This reduces fragmentation, repetition, and replication
25.Classification in Large Databases
Classification—a classical problem extensively studied by statisticians and machine learning researchers
Scalability: Classifying data sets with millions of examples and hundreds of attributes with reasonable speed
Why decision tree induction in data mining?
relatively faster learning speed (than other classification methods)
convertible to simple and easy to understand classification rules
can use SQL queries for accessing databases
comparable classification accuracy with other methods
Classification—a classical problem extensively studied by statisticians and machine learning researchers
Scalability: Classifying data sets with millions of examples and hundreds of attributes with reasonable speed
Why decision tree induction in data mining?
relatively faster learning speed (than other classification methods)
convertible to simple and easy to understand classification rules
can use SQL queries for accessing databases
comparable classification accuracy with other methods
26.Scalable Decision Tree Induction Methods in Data Mining Studies
SLIQ (EDBT’96 — Mehta et al.)
builds an index for each attribute and only class list and the current attribute list reside in memory
SPRINT (VLDB’96 — J. Shafer et al.)
constructs an attribute list data structure
PUBLIC (VLDB’98 — Rastogi & Shim)
integrates tree splitting and tree pruning: stop growing the tree earlier
RainForest (VLDB’98 — Gehrke, Ramakrishnan & Ganti)
separates the scalability aspects from the criteria that determine the quality of the tree
builds an AVC-list (attribute, value, class label)
SLIQ (EDBT’96 — Mehta et al.)
builds an index for each attribute and only class list and the current attribute list reside in memory
SPRINT (VLDB’96 — J. Shafer et al.)
constructs an attribute list data structure
PUBLIC (VLDB’98 — Rastogi & Shim)
integrates tree splitting and tree pruning: stop growing the tree earlier
RainForest (VLDB’98 — Gehrke, Ramakrishnan & Ganti)
separates the scalability aspects from the criteria that determine the quality of the tree
builds an AVC-list (attribute, value, class label)
27.Data Cube-Based Decision-Tree Induction
Integration of generalization with decision-tree induction (Kamber et al’97).
Classification at primitive concept levels
E.g., precise temperature, humidity, outlook, etc.
Low-level concepts, scattered classes, bushy classification-trees
Semantic interpretation problems.
Cube-based multi-level classification
Relevance analysis at multi-levels.
Information-gain analysis with dimension + level.
Integration of generalization with decision-tree induction (Kamber et al’97).
Classification at primitive concept levels
E.g., precise temperature, humidity, outlook, etc.
Low-level concepts, scattered classes, bushy classification-trees
Semantic interpretation problems.
Cube-based multi-level classification
Relevance analysis at multi-levels.
Information-gain analysis with dimension + level.
28.Presentation of Classification Results
29. Classification and Prediction
What is classification? What is prediction?
Issues regarding classification and prediction
Classification by decision tree induction
Bayesian Classification
Classification by backpropagation
Classification based on concepts from association rule mining
Other Classification Methods
Prediction
Classification accuracy
Summary
What is classification? What is prediction?
Issues regarding classification and prediction
Classification by decision tree induction
Bayesian Classification
Classification by backpropagation
Classification based on concepts from association rule mining
Other Classification Methods
Prediction
Classification accuracy
Summary
30.Bayesian Classification: Why?
Probabilistic learning: Calculate explicit probabilities for hypothesis, among the most practical approaches to certain types of learning problems
Incremental: Each training example can incrementally increase/decrease the probability that a hypothesis is correct. Prior knowledge can be combined with observed data.
Probabilistic prediction: Predict multiple hypotheses, weighted by their probabilities
Standard: Even when Bayesian methods are computationally intractable, they can provide a standard of optimal decision making against which other methods can be measured
Probabilistic learning: Calculate explicit probabilities for hypothesis, among the most practical approaches to certain types of learning problems
Incremental: Each training example can incrementally increase/decrease the probability that a hypothesis is correct. Prior knowledge can be combined with observed data.
Probabilistic prediction: Predict multiple hypotheses, weighted by their probabilities
Standard: Even when Bayesian methods are computationally intractable, they can provide a standard of optimal decision making against which other methods can be measured
31.Bayesian Theorem
32.Naïve Bayes Classifier (I)
33.Naive Bayesian Classifier (II)
34.Bayesian classification
35.Estimating a-posteriori probabilities
36.Naïve Bayesian Classification
37.Play-tennis example: estimating P(xi|C)
38.Play-tennis example: classifying X
39.The independence hypothesis…
40.Bayesian Belief Networks (I)
41.Bayesian Belief Networks (II)
Bayesian belief network allows a subset of the variables conditionally independent
A graphical model of causal relationships
Several cases of learning Bayesian belief networks
Given both network structure and all the variables: easy
Given network structure but only some variables
When the network structure is not known in advance
Bayesian belief network allows a subset of the variables conditionally independent
A graphical model of causal relationships
Several cases of learning Bayesian belief networks
Given both network structure and all the variables: easy
Given network structure but only some variables
When the network structure is not known in advance
42.Classification and Prediction
What is classification? What is prediction?
Issues regarding classification and prediction
Classification by decision tree induction
Bayesian Classification
Classification by backpropagation
Classification based on concepts from association rule mining
Other Classification Methods
Prediction
Classification accuracy
Summary
What is classification? What is prediction?
Issues regarding classification and prediction
Classification by decision tree induction
Bayesian Classification
Classification by backpropagation
Classification based on concepts from association rule mining
Other Classification Methods
Prediction
Classification accuracy
Summary
43.Neural Networks
Advantages
prediction accuracy is generally high
robust, works when training examples contain errors
output may be discrete, real-valued, or a vector of several discrete or real-valued attributes
fast evaluation of the learned target function
Criticism
long training time
difficult to understand the learned function (weights)
not easy to incorporate domain knowledge
Advantages
prediction accuracy is generally high
robust, works when training examples contain errors
output may be discrete, real-valued, or a vector of several discrete or real-valued attributes
fast evaluation of the learned target function
Criticism
long training time
difficult to understand the learned function (weights)
not easy to incorporate domain knowledge
44.A Neuron
45.Network Training
The ultimate objective of training
obtain a set of weights that makes almost all the tuples in the training data classified correctly
Steps
Initialize weights with random values
Feed the input tuples into the network one by one
For each unit
Compute the net input to the unit as a linear combination of all the inputs to the unit
Compute the output value using the activation function
Compute the error
Update the weights and the bias
The ultimate objective of training
obtain a set of weights that makes almost all the tuples in the training data classified correctly
Steps
Initialize weights with random values
Feed the input tuples into the network one by one
For each unit
Compute the net input to the unit as a linear combination of all the inputs to the unit
Compute the output value using the activation function
Compute the error
Update the weights and the bias
46.Multi-Layer Perceptron
47.Network Pruning and Rule Extraction
48.Classification and Prediction
What is classification? What is prediction?
Issues regarding classification and prediction
Classification by decision tree induction
Bayesian Classification
Classification by backpropagation
Classification based on concepts from association rule mining
Other Classification Methods
Prediction
Classification accuracy
Summary
What is classification? What is prediction?
Issues regarding classification and prediction
Classification by decision tree induction
Bayesian Classification
Classification by backpropagation
Classification based on concepts from association rule mining
Other Classification Methods
Prediction
Classification accuracy
Summary
49.Association-Based Classification
Several methods for association-based classification
ARCS: Quantitative association mining and clustering of association rules (Lent et al’97)
It beats C4.5 in (mainly) scalability and also accuracy
Associative classification: (Liu et al’98)
It mines high support and high confidence rules in the form of “cond_set => y”, where y is a class label
CAEP (Classification by aggregating emerging patterns) (Dong et al’99)
Emerging patterns (EPs): the itemsets whose support increases significantly from one class to another
Mine Eps based on minimum support and growth rate
Several methods for association-based classification
ARCS: Quantitative association mining and clustering of association rules (Lent et al’97)
It beats C4.5 in (mainly) scalability and also accuracy
Associative classification: (Liu et al’98)
It mines high support and high confidence rules in the form of “cond_set => y”, where y is a class label
CAEP (Classification by aggregating emerging patterns) (Dong et al’99)
Emerging patterns (EPs): the itemsets whose support increases significantly from one class to another
Mine Eps based on minimum support and growth rate
50.Classification and Prediction
What is classification? What is prediction?
Issues regarding classification and prediction
Classification by decision tree induction
Bayesian Classification
Classification by backpropagation
Classification based on concepts from association rule mining
Other Classification Methods
Prediction
Classification accuracy
Summary
What is classification? What is prediction?
Issues regarding classification and prediction
Classification by decision tree induction
Bayesian Classification
Classification by backpropagation
Classification based on concepts from association rule mining
Other Classification Methods
Prediction
Classification accuracy
Summary
51.Other Classification Methodsk-nearest neighbor classifier
case-based reasoning
Genetic algorithm
Rough set approach
Fuzzy set approaches
case-based reasoning
Genetic algorithm
Rough set approach
Fuzzy set approaches
52.Instance-Based Methods
53.The k-Nearest Neighbor Algorithm
54.Discussion on the k-NN Algorithm
55.Case-Based Reasoning
56.Also uses: lazy evaluation + analyze similar instances
Difference: Instances are not “points in a Euclidean space”
Example: Water faucet problem in CADET (Sycara et al’92)
Methodology
Instances represented by rich symbolic descriptions (e.g., function graphs)
Multiple retrieved cases may be combined
Tight coupling between case retrieval, knowledge-based reasoning, and problem solving
Research issues
Indexing based on syntactic similarity measure, and when failure, backtracking, and adapting to additional cases
Difference: Instances are not “points in a Euclidean space”
Example: Water faucet problem in CADET (Sycara et al’92)
Methodology
Instances represented by rich symbolic descriptions (e.g., function graphs)
Multiple retrieved cases may be combined
Tight coupling between case retrieval, knowledge-based reasoning, and problem solving
Research issues
Indexing based on syntactic similarity measure, and when failure, backtracking, and adapting to additional cases
57.Genetic Algorithms
GA: based on an analogy to biological evolution
Each rule is represented by a string of bits
An initial population is created consisting of randomly generated rules
e.g., IF A1 and Not A2 then C2 can be encoded as 100
Based on the notion of survival of the fittest, a new population is formed to consists of the fittest rules and their offsprings
The fitness of a rule is represented by its classification accuracy on a set of training examples
Offsprings are generated by crossover and mutation
GA: based on an analogy to biological evolution
Each rule is represented by a string of bits
An initial population is created consisting of randomly generated rules
e.g., IF A1 and Not A2 then C2 can be encoded as 100
Based on the notion of survival of the fittest, a new population is formed to consists of the fittest rules and their offsprings
The fitness of a rule is represented by its classification accuracy on a set of training examples
Offsprings are generated by crossover and mutation
58.Rough Set Approach
Rough sets are used to approximately or “roughly” define equivalent classes
A rough set for a given class C is approximated by two sets: a lower approximation (certain to be in C) and an upper approximation (cannot be described as not belonging to C)
Finding the minimal subsets (reducts) of attributes (for feature reduction) is NP-hard but a discernibility matrix is used to reduce the computation intensity
Rough sets are used to approximately or “roughly” define equivalent classes
A rough set for a given class C is approximated by two sets: a lower approximation (certain to be in C) and an upper approximation (cannot be described as not belonging to C)
Finding the minimal subsets (reducts) of attributes (for feature reduction) is NP-hard but a discernibility matrix is used to reduce the computation intensity
59.Fuzzy Set Approaches
Fuzzy logic uses truth values between 0.0 and 1.0 to represent the degree of membership (such as using fuzzy membership graph)
Attribute values are converted to fuzzy values
e.g., income is mapped into the discrete categories {low, medium, high} with fuzzy values calculated
For a given new sample, more than one fuzzy value may apply
Each applicable rule contributes a vote for membership in the categories
Typically, the truth values for each predicted category are summed
Fuzzy logic uses truth values between 0.0 and 1.0 to represent the degree of membership (such as using fuzzy membership graph)
Attribute values are converted to fuzzy values
e.g., income is mapped into the discrete categories {low, medium, high} with fuzzy values calculated
For a given new sample, more than one fuzzy value may apply
Each applicable rule contributes a vote for membership in the categories
Typically, the truth values for each predicted category are summed
60.Classification and Prediction
What is classification? What is prediction?
Issues regarding classification and prediction
Classification by decision tree induction
Bayesian Classification
Classification by backpropagation
Classification based on concepts from association rule mining
Other Classification Methods
Prediction
Classification accuracy
Summary
What is classification? What is prediction?
Issues regarding classification and prediction
Classification by decision tree induction
Bayesian Classification
Classification by backpropagation
Classification based on concepts from association rule mining
Other Classification Methods
Prediction
Classification accuracy
Summary
61.What Is Prediction?
Prediction is similar to classification
First, construct a model
Second, use model to predict unknown value
Major method for prediction is regression
Linear and multiple regression
Non-linear regression
Prediction is different from classification
Classification refers to predict categorical class label
Prediction models continuous-valued functions
Prediction is similar to classification
First, construct a model
Second, use model to predict unknown value
Major method for prediction is regression
Linear and multiple regression
Non-linear regression
Prediction is different from classification
Classification refers to predict categorical class label
Prediction models continuous-valued functions
62.Predictive Modeling in Databases
Predictive modeling: Predict data values or construct generalized linear models based on the database data.
One can only predict value ranges or category distributions
Method outline:
Minimal generalization
Attribute relevance analysis
Generalized linear model construction
Prediction
Determine the major factors which influence the prediction
Data relevance analysis: uncertainty measurement, entropy analysis, expert judgement, etc.
Multi-level prediction: drill-down and roll-up analysis
Predictive modeling: Predict data values or construct generalized linear models based on the database data.
One can only predict value ranges or category distributions
Method outline:
Minimal generalization
Attribute relevance analysis
Generalized linear model construction
Prediction
Determine the major factors which influence the prediction
Data relevance analysis: uncertainty measurement, entropy analysis, expert judgement, etc.
Multi-level prediction: drill-down and roll-up analysis
63.Regress Analysis and Log-Linear Models in Prediction
64. Locally Weighted Regression
65.Prediction: Numerical Data
66.Prediction: Categorical Data
67.Classification and Prediction
What is classification? What is prediction?
Issues regarding classification and prediction
Classification by decision tree induction
Bayesian Classification
Classification by backpropagation
Classification based on concepts from association rule mining
Other Classification Methods
Prediction
Classification accuracy
Summary
What is classification? What is prediction?
Issues regarding classification and prediction
Classification by decision tree induction
Bayesian Classification
Classification by backpropagation
Classification based on concepts from association rule mining
Other Classification Methods
Prediction
Classification accuracy
Summary
68.Classification Accuracy: Estimating Error Rates
69.Boosting and Bagging
Boosting increases classification accuracy
Applicable to decision trees or Bayesian classifier
Learn a series of classifiers, where each classifier in the series pays more attention to the examples misclassified by its predecessor
Boosting requires only linear time and constant space
Boosting increases classification accuracy
Applicable to decision trees or Bayesian classifier
Learn a series of classifiers, where each classifier in the series pays more attention to the examples misclassified by its predecessor
Boosting requires only linear time and constant space
70.Boosting Technique (II) — Algorithm
71.Summary
Classification is an extensively studied problem (mainly in statistics, machine learning & neural networks)
Classification is probably one of the most widely used data mining techniques with a lot of extensions
Scalability is still an important issue for database applications: thus combining classification with database techniques should be a promising topic
Research directions: classification of non-relational data, e.g., text, spatial, multimedia, etc..
Classification is an extensively studied problem (mainly in statistics, machine learning & neural networks)
Classification is probably one of the most widely used data mining techniques with a lot of extensions
Scalability is still an important issue for database applications: thus combining classification with database techniques should be a promising topic
Research directions: classification of non-relational data, e.g., text, spatial, multimedia, etc..
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