Intelligent fault diagnosis of rotating machinery using infrared thermal image
Expert Systems with Applications: An International Journal
Selecting feature subset via constraint association rules
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
A novel feature subset selection algorithm based on association rule mining
Intelligent Data Analysis
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This paper presents extended Relief algorithms and their use in instance-based feature filtering for document feature selection.The Relief algorithms are general and successful feature estimators that detect conditional dependencies of features between instances, and are applied in the preprocessing step for document classification and regression. Since the introduction the Relief algorithm, many kinds of extended Relief algorithms have been suggested as solutions to problems of redundancy, irrelevant and noisy features as well as Relief algorithm's limitations in two-class and multi-class datasets. In this paper, we introduce additional problems including the negative influence of computation similarities and weights caused by the small number offeatures in an instance, the absence of nearest Hits or nearest Misses for some instances using Relief algorithms, and other of problems. We suggest new extended Relief algorithms to solve those problems, having in the course of our research, and experimented on the estimation of the quality of features from instances, and classified datasets, and having compared theresults of the new extended Relief algorithms. Indeed in the experimental results, the new extended Relief algorithms showed better performances for all of the datasets than did the Relief algorithms