C4.5: programs for machine learning
C4.5: programs for machine learning
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
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth 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
An introduction to variable and feature selection
The Journal of Machine Learning Research
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Consistency-based search in feature selection
Artificial Intelligence
Testing the significance of attribute interactions
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Fast Binary Feature Selection with Conditional Mutual Information
The Journal of Machine Learning Research
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A new approach to classification based on association rule mining
Decision Support Systems
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)
Searching for interacting features in subset selection
Intelligent Data Analysis
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
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A constrained frequent pattern mining system for handling aggregate constraints
Proceedings of the 16th International Database Engineering & Applications Sysmposium
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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, and detects the redundant feature values with constraint association rules whose consequent and antecedent are both single feature value. After that, it eliminates the redundant feature values, and obtains the feature subset by mapping the relevant feature values to corresponding features. The efficiency and effectiveness of FEAST are tested upon both synthetic and real world data sets, and the classification results of the three different types of classifiers (including Naive Bayes, C4.5 and PART) with the other four representative feature subset selection algorithms (including CFS, FCBF, INTERACT and associative-based FSBAR) were compared. The results on 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 outperformed other feature subset selection algorithms in terms of average classification accuracy and Win/Draw/Loss record.