智能系统学报
智能繫統學報
지능계통학보
CAAI TRANSACTIONS ON INTELLIGENT SYSTEMS
2013年
2期
183-186
,共4页
辨识矩阵%属性约简%信息冗余%人工智能%机器学习%属性选择%属性删除
辨識矩陣%屬性約簡%信息冗餘%人工智能%機器學習%屬性選擇%屬性刪除
변식구진%속성약간%신식용여%인공지능%궤기학습%속성선택%속성산제
discernibility matrix%attribute reduction%information redundancy%artificial intelligence%machine learning%attribute selection%attribute deletion
属性约简能有效地消除信息冗余，广泛应用于人工智能、机器学习。通过实例指出基于辨识矩阵的经典的属性约简方法存在不能得到约简的可能性，仍具有冗余性。因此，提出了综合属性选择和删除算法的辨识矩阵属性约简方法，并有效解决该问题。通过 UCI 标准数据集验证表明，新方法比经典方法进一步减少了属性的个数，凸显其实用性和有效性。
屬性約簡能有效地消除信息冗餘，廣汎應用于人工智能、機器學習。通過實例指齣基于辨識矩陣的經典的屬性約簡方法存在不能得到約簡的可能性，仍具有冗餘性。因此，提齣瞭綜閤屬性選擇和刪除算法的辨識矩陣屬性約簡方法，併有效解決該問題。通過 UCI 標準數據集驗證錶明，新方法比經典方法進一步減少瞭屬性的箇數，凸顯其實用性和有效性。
속성약간능유효지소제신식용여，엄범응용우인공지능、궤기학습。통과실례지출기우변식구진적경전적속성약간방법존재불능득도약간적가능성，잉구유용여성。인차，제출료종합속성선택화산제산법적변식구진속성약간방법，병유효해결해문제。통과 UCI 표준수거집험증표명，신방법비경전방법진일보감소료속성적개수，철현기실용성화유효성。
Attribute reduction has been defined as a method for removing information redundancy effectively , which has been widely applied to artificial intelligence , and machine learning.However, an example demonstrates classi-cal attribute reduction approaches based on discernibility matrix may not get a reduction with redundancy .There-fore, an attribute reduction based on discernibility matrix combining attribute selection and deletion was proposed and thus, the problem was solved effectively.Moreover, UCI standard data sets provide further explanations on the feasibility, effectiveness, and as well as additional information on reducing the number of attributes without the classical approaches.
