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Information 2017, 8(4), 159;

sCwc/sLcc: Highly Scalable Feature Selection Algorithms

Graduate School of Applied Informatics, University of Hyogo, Kobe 651-2197, Japan
Information Networking Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Computer Centre, Gakushuin University, Tokyo 171-0031, Japan
Institude of Economic Research, Chiba University of Commerce, Chiba 272-8512, Japan
Center for Digital Humanities, University of California Las Angeles; Los Angeles, CA 90095, USA
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Received: 31 October 2017 / Revised: 1 December 2017 / Accepted: 2 December 2017 / Published: 6 December 2017
(This article belongs to the Special Issue Feature Selection for High-Dimensional Data)
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Feature selection is a useful tool for identifying which features, or attributes, of a dataset cause or explain the phenomena that the dataset describes, and improving the efficiency and accuracy of learning algorithms for discovering such phenomena. Consequently, feature selection has been studied intensively in machine learning research. However, while feature selection algorithms that exhibit excellent accuracy have been developed, they are seldom used for analysis of high-dimensional data because high-dimensional data usually include too many instances and features, which make traditional feature selection algorithms inefficient. To eliminate this limitation, we tried to improve the run-time performance of two of the most accurate feature selection algorithms known in the literature. The result is two accurate and fast algorithms, namely sCwc and sLcc. Multiple experiments with real social media datasets have demonstrated that our algorithms improve the performance of their original algorithms remarkably. For example, we have two datasets, one with 15,568 instances and 15,741 features, and another with 200,569 instances and 99,672 features. sCwc performed feature selection on these datasets in 1.4 seconds and in 405 seconds, respectively. In addition, sLcc has turned out to be as fast as sCwc on average. This is a remarkable improvement because it is estimated that the original algorithms would need several hours to dozens of days to process the same datasets. In addition, we introduce a fast implementation of our algorithms: sCwc does not require any adjusting parameter, while sLcc requires a threshold parameter, which we can use to control the number of features that the algorithm selects. View Full-Text
Keywords: feature selection; consistency; high-dimensional data; scalability feature selection; consistency; high-dimensional data; scalability

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Shin, K.; Kuboyama, T.; Hashimoto, T.; Shepard, D. sCwc/sLcc: Highly Scalable Feature Selection Algorithms. Information 2017, 8, 159.

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