A method for attributes reduction based on scan vector
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摘要: 針對粗糙集理論中屬性約簡問題,提出了一種基于掃描向量的屬性約簡方法.根據粗糙集理論知識,定義了一個新概念——差別向量,利用差別向量將信息表轉換成差別向量組;根據差別向量的結構特征,定義了差別向量加法法則;運用這個加法法則僅需對差別向量組掃描一次,就可以形成結構簡潔卻能代表原信息表屬性特征的掃描向量.以掃描向量中的屬性頻率項作為屬性約簡搜索的啟發信息,提高了屬性約簡效率.數值實例及數據庫測試的結果表明該屬性約簡算法是有效可行的.Abstract: In order to deal with attributes reduction, one of the major problems in rough set theory, an attributes reduction algorithm was proposed based on scan vector, and a new conception of discernible vector was defined by which the information table can be transformed into discernible vector sets. Depending on the structural feature of the discernible vector, a plus rule for the discernible vector sets was defined, and a scan vector with concise structure but representing the information table can be obtained through scanning the discernible vector just one time. The item of attribute frequency in the scan vector was taken as heuristic information to improve the efficiency of attributes reduction. An illustration and experimental results indicate that the method proposed is much more effective.
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Key words:
- rough set /
- information table /
- attributes reduction /
- discernible attributes set /
- scan vector
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