应用科技
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Applied Science and Technology
2015年
5期
10-13
,共4页
模式分类%近邻原则%k-近邻%k-近质心近邻%局部权重
模式分類%近鄰原則%k-近鄰%k-近質心近鄰%跼部權重
모식분류%근린원칙%k-근린%k-근질심근린%국부권중
pattern classification%nearest neighbor rule%k-nearest neighbor rule%k-nearest centroid neighbor rule%local weight
k-近质心近邻原则是k-近邻原则的一种有效扩展,是有效的模式分类方法之一. k-近质心近邻原则容易受到局外点的影响;同时,所有的k-近质心近邻点在分类决策时具有相同的权重和分类贡献率,这显然是不合理的. 为了解决这一问题,考虑到质心近邻在模式分类问题上具有近邻特性和空间分布特性,提出一种基于局部权重的近质心近邻算法,实验结果表明该LWKNCN算法在分类精度上优于传统的KNN算法和KNCN算法.
k-近質心近鄰原則是k-近鄰原則的一種有效擴展,是有效的模式分類方法之一. k-近質心近鄰原則容易受到跼外點的影響;同時,所有的k-近質心近鄰點在分類決策時具有相同的權重和分類貢獻率,這顯然是不閤理的. 為瞭解決這一問題,攷慮到質心近鄰在模式分類問題上具有近鄰特性和空間分佈特性,提齣一種基于跼部權重的近質心近鄰算法,實驗結果錶明該LWKNCN算法在分類精度上優于傳統的KNN算法和KNCN算法.
k-근질심근린원칙시k-근린원칙적일충유효확전,시유효적모식분류방법지일. k-근질심근린원칙용역수도국외점적영향;동시,소유적k-근질심근린점재분류결책시구유상동적권중화분류공헌솔,저현연시불합리적. 위료해결저일문제,고필도질심근린재모식분류문제상구유근린특성화공간분포특성,제출일충기우국부권중적근질심근린산법,실험결과표명해LWKNCN산법재분류정도상우우전통적KNN산법화KNCN산법.
The k-nearest centroid neighbor rule ( KNCN) , as an effective extension of the k-Nearest Neighbor rule ( KNN) , is one of the effective algorithms in pattern classification. The KNCN is prone to be seriously influenced by the existing outliers. At the same time, all the k-nearest centroid neighbor samples have the same weight and the same contribution to classification results, which is unreasonable. To solve this problem, this paper proposes a nea-rest centroid neighbor algorithm based on the local weight, taking account of the proximity and spatial distribution characteristics of the neighbors for a query pattern. The experimental results show that the classification accuracy of LWKNCN is better than that of the traditional KNN algorithm and KNCN algorithm.
