K-最近鄰分類技術(shù)的改進(jìn)算法
An Improved K-Nearest Neighbor Algorithm
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摘要: 該文提出了一種改進(jìn)的K-最近鄰分類算法。該算法首先將訓(xùn)練事例集中的每一類樣本進(jìn)行聚類,既減小了訓(xùn)練事例集的數(shù)據(jù)量,又去除了孤立點(diǎn),大大提高了算法的快速性和預(yù)測精度,從而使該算法適用于海量數(shù)據(jù)集的情況。同時,在算法中根據(jù)每個屬性對分類貢獻(xiàn)的大小,采用神經(jīng)網(wǎng)絡(luò)計(jì)算其權(quán)重,將這些屬性權(quán)重用在最近鄰計(jì)算中,從而提高了算法的分類精度。在幾個標(biāo)準(zhǔn)數(shù)據(jù)庫和實(shí)際數(shù)據(jù)庫上的實(shí)驗(yàn)結(jié)果表明,該算法適合于對復(fù)雜而數(shù)據(jù)量比較大的數(shù)據(jù)庫進(jìn)行分類。
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關(guān)鍵詞:
- K-最近鄰; 聚類; 權(quán)值調(diào)整; 分類
Abstract: This paper presents a improved K-NN algorithm. The CURE clustering is carried out to select the subset of the training set. It can reduce the volume of the training set and omit the outlier. Therefore it can lead both to computational efficiency and to higher classification accuracy. In the algorithm, the weights of each feature are learned using neural network. The feature weights are used in the nearest measure computation such that the important features contribute more in the nearest measure. Experiments on several UCI databases and practical data sets show the efficiency of the algorithm. -
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