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
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Feature Selection as a Preprocessing Step for Hierarchical Clustering
ICML '99 Proceedings of the Sixteenth 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
Efficiently handling feature redundancy in high-dimensional data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Feature Subset Selection and Feature Ranking for Multivariate Time Series
IEEE Transactions on Knowledge and Data Engineering
An efficient k nearest neighbor search for multivariate time series
Information and Computation
Network intrusion detection through Adaptive Sub-Eigenspace Modeling in multiagent systems
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Using kNN model for automatic feature selection
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Improved feature selection algorithm based on SVM and correlation
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
CLeVer: a feature subset selection technique for multivariate time series
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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Feature selection is frequently used in data pre-processing for data mining. When the training data set is too large, sampling is commonly used to overcome the difficulty. This work investigates the applicability of active sampling in feature selection in a filter model setting. Our objective is to partition data by taking advantage of class information so as to achieve the same or better performance for feature selection with fewer but more relevant instances than random sampling. Two versions of active feature selection that employ class information are proposed and empirically evaluated. In comparison with random sampling, we conduct extensive experiments with benchmark data sets, and analyze reasons why class-based active feature selection works in the way it does. The results will help us deal with large data sets and provide ideas to scale up other feature selection algorithms.