A feature weighted penalty based dissimilarity measure for k-nearest neighbor classification with missing features
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- 作者：Shounak Dattaa ; Debaleena Misrab ; Swagatam Das ; a ; swagatamdas19@yahoo.co.in" class="auth_mail" title="E-mail the corresponding author
- 关键词：kNN classifier ; Missing features ; Dissimilarity measure ; Penalized dissimilarity
- 刊名：Pattern Recognition Letters
- 出版年：2016
- 出版时间：1 September 2016
- 年：2016
- 卷：80
- 期：Complete
- 页码：231-237
- 全文大小：999 K
文摘
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kNN-FWPD classifier is proposed with FWPD as the underlying dissimilarity measure.
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kNN-FWPD classifier can be directly applied to datasets having missing features.
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The proposed classifier has similar time complexity compared to the kNN classifier.
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Experiments are conducted on 4 types of missingness: MCAR, MAR, MNAR1, and MNAR2.
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kNN-FWPD is found to outperform ZI, AI, and kNNI in terms of classification accuracy.