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
Searching for interacting features
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Consistency-Based Feature Selection
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
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This paper proposes a new consistency-based feature selection algorithm, which presents a new balance to the fundamental tradeoff between the quality of outputs of feature selection algorithms and their efficiency. Consistency represents the extent of corrective relevance of features to classification, and hence, consistency-based feature selection algorithms such as INTERACT, LCC and CCC can select relevant features more correctly by taking interaction among features into account. INTERACT and LCC are fast by employing the linear search strategy. By contrast, CCC is slow, since it is based on the complete search strategy, but can output feature subsets of higher quality. The algorithm that we propose in this paper, on the other hand, takes the steepest descent method as the search strategy. Consequently, it can find better solutions than INTERACT and LCC, and simultaneously restrains the increase in computational complexity within a reasonable level: it evaluates $(|{\mathcal F}| + |{\tilde {\mathcal F}}|)(|{\mathcal F}| - |{\tilde {\mathcal F}}| + 1)/2$ feature subsets to output ${\tilde {\mathcal F}}$. We prove effectiveness of the new algorithm through experiments.