Add to ORKGAbstract. The automatic induction of classification rules from examples is an important technique used in data mining. One of the problems encountered is the overfitting of rules to training data. This paper describes a means of reducing overfitting known as J-pruning, based on the J-measure, an information theoretic means of quantifying the information content of a rule, and examines its effectiveness in the presence of noisy data for two rule induction algorithms: one where the rules are generated via the intermediate representation of a decision tree and one where rules are generated directly from examples.
Automatic generation of classification rules has been an increasingly popular technique in commercia...
This paper compares five methods for pruning decision trees, developed from sets of examples. When u...
Abstract — Decision trees are few of the most extensively researched domains in Knowledge Discovery....
Abstract: The automatic induction of classification rules from examples is an important technique us...
Learning of classification rules is a popular approach of machine learning, which can be achieved th...
When learning is based on noisy data, the induced rule sets have a tendency to overfit the training ...
Generating classification rules from data often leads to large sets of rules that need to be pruned....
Pre-Pruning and Post-Pruning are two standard methods of dealing with noise in decision tree learnin...
This paper examines the induction of classification rules from examples using real-world data. Real-...
Induction methods have recently been found to be useful in a wide variety of business related proble...
Prism is a modular classification rule generation method based on the ‘separate and conquer’ approa...
AbstractWe present ELEM2, a machine learning system that induces classification rules from a set of ...
Rule-based classification is considered an important task of data classification.The ant-mining rule...
Compression measures used in inductive learners, such as measures based on the minimum description l...
Top-down induction of decision trees has been observed to suer from the inadequate functioning of th...
Automatic generation of classification rules has been an increasingly popular technique in commercia...
This paper compares five methods for pruning decision trees, developed from sets of examples. When u...
Abstract — Decision trees are few of the most extensively researched domains in Knowledge Discovery....
Abstract: The automatic induction of classification rules from examples is an important technique us...
Learning of classification rules is a popular approach of machine learning, which can be achieved th...
When learning is based on noisy data, the induced rule sets have a tendency to overfit the training ...
Generating classification rules from data often leads to large sets of rules that need to be pruned....
Pre-Pruning and Post-Pruning are two standard methods of dealing with noise in decision tree learnin...
This paper examines the induction of classification rules from examples using real-world data. Real-...
Induction methods have recently been found to be useful in a wide variety of business related proble...
Prism is a modular classification rule generation method based on the ‘separate and conquer’ approa...
AbstractWe present ELEM2, a machine learning system that induces classification rules from a set of ...
Rule-based classification is considered an important task of data classification.The ant-mining rule...
Compression measures used in inductive learners, such as measures based on the minimum description l...
Top-down induction of decision trees has been observed to suer from the inadequate functioning of th...
Automatic generation of classification rules has been an increasingly popular technique in commercia...
This paper compares five methods for pruning decision trees, developed from sets of examples. When u...
Abstract — Decision trees are few of the most extensively researched domains in Knowledge Discovery....