基于影響函數(shù)的k-近鄰分類
doi: 10.11999/JEIT141433
基金項目:
國家自然科學(xué)基金(61170223)和河南省教育廳科學(xué)技術(shù)研究重點項目(14A520016)資助課題
k-nearest Neighbor Classification Based on Influence Function
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摘要: 分類是一種監(jiān)督學(xué)習(xí)方法，通過在訓(xùn)練數(shù)據(jù)集學(xué)習(xí)模型判定未知樣本的類標(biāo)號。與傳統(tǒng)的分類思想不同，該文從影響函數(shù)的角度理解分類，即從訓(xùn)練樣本集對未知樣本的影響來判定未知樣本的類標(biāo)號。首先介紹基于影響函數(shù)分類的思想；其次給出影響函數(shù)的定義，設(shè)計3種影響函數(shù)；最后基于這3種影響函數(shù)，提出基于影響函數(shù)的k-近鄰(kNN)分類方法。并將該方法應(yīng)用到非平衡數(shù)據(jù)集分類中。在18個UCI數(shù)據(jù)集上的實驗結(jié)果表明，基于影響函數(shù)的k-近鄰分類方法的分類性能好于傳統(tǒng)的k-近鄰分類方法，且對非平衡數(shù)據(jù)集分類有效。
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關(guān)鍵詞:
- 數(shù)據(jù)挖掘 /
- 監(jiān)督學(xué)習(xí) /
- 非平衡數(shù)據(jù)集分類 /
- 影響函數(shù) /
- k-近鄰
Abstract: Classification is a supervised learning. It determines the class label of an unlabeled instance by learning model based on the training dataset. Unlike traditional classification, this paper views classification problem from another perspective, that is influential function. That is, the class label of an unlabeled instance is determined by the influence of the training data set. Firstly, the idea of classification is introduced based on influence function. Secondly, the definition of influence function is given and three influence functions are designed. Finally, this paper proposes k-nearest neighbor classification method based on these three influence functions and applies it to the classification of imbalanced data sets. The experimental results on 18 UCI data sets show that the proposed method improves effectively the k-nearest neighbor generalization ability. Besides, the proposed method is effective for imbalanced classification. -
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