Learning Boolean concepts in the presence of many irrelevant features
Artificial Intelligence
Consistency-based search in feature selection
Artificial Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Searching for interacting features
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A Consistency-Constrained Feature Selection Algorithm with the Steepest Descent Method
MDAI '09 Proceedings of the 6th International Conference on Modeling Decisions for Artificial Intelligence
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Engineering Applications of Artificial Intelligence
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Feature selection, the job to select features relevant to classification, is a central problem of machine learning. Inconsistency rate is known as an effective measure to evaluate consistency (relevance) of feature subsets, and INTERACT, a state-of-the-art feature selection algorithm, takes advantage of it. In this paper, we shows that inconsistency rate is not the unique measure of consistency by introducing two new consistency measures, and also, show that INTERACT has the important deficiency that it fails for particular types of probability distributions. To fix the deficiency, we propose two new algorithms, which have flexibility of taking advantage of any of the new measures as well as inconsistency rate. Furthermore, through experiments, we compare the three consistency measures, and prove effectiveness of the new algorithms.