Instance-Based Learning Algorithms
Machine Learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Machine Learning
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Computational Genome Analysis: An Introduction
Computational Genome Analysis: An Introduction
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Feature selection for optimizing traffic classification
Computer Communications
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Feature selection concerns the problem of selecting a number of important features (w.r.t. the class labels) in order to build accurate prediction models. Traditional feature selection methods, however, fail to take the sample distributions into the consideration which may lead to poor predictions for minority class examples. Due to the sophistication and the cost involved in the data collection process, many applications, such as Biomedical research, commonly face biased data collections with one class of examples (e.g., diseased samples) significantly less than other classes (e.g., normal samples). For these applications, the minority class examples, such as disease samples, credit card frauds, and network intrusions, are only a small portion of the data collections but deserve full attentions for accurate prediction. In this paper, we propose three filtering techniques, Higher Weight (HW), Differential Minority Repeat (DMR) and Balanced Minority Repeat (BMR), to identify important features from biased data collections. Experimental comparisons with the ReliefF method on five datasets demonstrate the effectiveness of the proposed methods in selecting informative features from data with biased sample distributions.