Instance-Based Learning Algorithms
Machine Learning
Constructive induction using a non-greedy strategy for feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Learning Boolean concepts in the presence of many irrelevant features
Artificial Intelligence
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Information-theoretic algorithm for feature selection
Pattern Recognition Letters
Characterization of Classification Algorithms
EPIA '95 Proceedings of the 7th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Consistency Based Feature Selection
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Chi2: Feature Selection and Discretization of Numeric Attributes
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
Feature Selection Algorithms: A Survey and Experimental Evaluation
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
An introduction to variable and feature selection
The Journal of Machine Learning Research
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
On Data and Algorithms: Understanding Inductive Performance
Machine Learning
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Redundancy based feature selection for microarray data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Testing the significance of attribute interactions
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Fast Binary Feature Selection with Conditional Mutual Information
The Journal of Machine Learning Research
A new approach to classification based on association rule mining
Decision Support Systems
Distances between Data Sets Based on Summary Statistics
The Journal of Machine Learning Research
Extended Relief Algorithms in Instance-Based Feature Filtering
ALPIT '07 Proceedings of the Sixth International Conference on Advanced Language Processing and Web Information Technology (ALPIT 2007)
Feature Selection Based on a New Dependency Measure
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01
Searching for interacting features in subset selection
Intelligent Data Analysis
A filter model for feature subset selection based on genetic algorithm
Knowledge-Based Systems
Feature subset selection in large dimensionality domains
Pattern Recognition
Feature Selection Algorithm Based on Association Rules Mining Method
ICIS '09 Proceedings of the 2009 Eigth IEEE/ACIS International Conference on Computer and Information Science
On learning algorithm selection for classification
Applied Soft Computing
Mining of Attribute Interactions Using Information Theoretic Metrics
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
A Weighted Voting-Based Associative Classification Algorithm
The Computer Journal
The feature selection problem: traditional methods and a new algorithm
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
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In this paper, a novel feature selection algorithm FEAST is proposed based on association rule mining. The proposed algorithm first mines association rules from a data set; then, it identifies the relevant and interactive feature values with the constraint association rules whose consequent is the target concept, detects and eliminates the redundant feature values with the constraint association rules whose consequent and antecedent are both of single feature value. Finally, it obtains the feature subset by mapping the feature values to the corresponding features. As the support and confidence thresholds are two important parameters in association rule mining and play a vital role in FEAST, a partial least square regression PLSR based threshold prediction method is presented as well. The effectiveness of FEAST is tested on both synthetic and real world data sets, and the classification results of five different types of classifiers with seven representative feature selection algorithms are compared. The results on the synthetic data sets show that FEAST can effectively identify irrelevant and redundant features while reserving interactive ones. The results on the real world data sets show that FEAST outperforms other feature selection algorithms in terms of classification accuracies. In addition, the PLSR based threshold prediction method is performed on the real world data sets, and the results show it works well in recommending proper support and confidence thresholds for FEAST.