The theory of filter based feature selection and high-performance algorithms
| Project/Area Number |
26280090
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Partial Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Gakushuin University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
申 吉浩 兵庫県立大学, 応用情報科学研究科, 教授 (60523587)
チャクラボルティ バサビ 岩手県立大学, ソフトウェア情報学部, 教授 (90305293)
橋本 隆子 千葉商科大学, 商経学部, 教授 (80551697)
川前 徳章 東京電機大学, 公私立大学の部局等, 研究員 (30447031)
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| Project Period (FY) |
2014-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥15,860,000 (Direct Cost: ¥12,200,000、Indirect Cost: ¥3,660,000)
Fiscal Year 2016: ¥6,110,000 (Direct Cost: ¥4,700,000、Indirect Cost: ¥1,410,000)
Fiscal Year 2015: ¥6,240,000 (Direct Cost: ¥4,800,000、Indirect Cost: ¥1,440,000)
Fiscal Year 2014: ¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
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| Keywords | 特徴選択 / カテゴリカルデータ / 一貫性指標 / 変数間相互作用 / 変数選択 / フィルター型 / トピック抽出 / アルゴリズム / 疎データ / 機械学習 |
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| Outline of Final Research Achievements |
We focus on feature selection algorithms that extract minimal subsets of features relevant to class labels from categorical data with high dimensional feature space. Filter-based feature selection consists of two important components; consistency measures between feature sets and class labels, and search strategies for minimal feature sets . Through theoretical and empirical analysis on these two components, we designed and implemented a very fast feature selection algorithm with high accuracy and scalability. We applied this algorithm to two applications; topic extraction from tweets, and pattern acquisition from graph-structured data.
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Report
(5 results)
Research Products
(44 results)
