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An Effective Feature Selection Method Based on Pair-Wise Feature Proximity for High Dimensional Low Sample Size Data

機(jī)譯：一種基于對(duì)偶特征的有效特征選擇方法 ??接近高維低樣本數(shù)據(jù)

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摘要

Feature selection has been studied widely in the literature. However, theefficacy of the selection criteria for low sample size applications isneglected in most cases. Most of the existing feature selection criteria arebased on the sample similarity. However, the distance measures becomeinsignificant for high dimensional low sample size (HDLSS) data. Moreover, thevariance of a feature with a few samples is pointless unless it represents thedata distribution efficiently. Instead of looking at the samples in groups, weevaluate their efficiency based on pairwise fashion. In our investigation, wenoticed that considering a pair of samples at a time and selecting the featuresthat bring them closer or put them far away is a better choice for featureselection. Experimental results on benchmark data sets demonstrate theeffectiveness of the proposed method with low sample size, which outperformsmany other state-of-the-art feature selection methods.
機(jī)譯：特征選擇已在文獻(xiàn)中得到廣泛研究。然而，在大多數(shù)情況下，低樣本量應(yīng)用選擇標(biāo)準(zhǔn)的有效性被忽略?，F(xiàn)有的大多數(shù)特征選擇標(biāo)準(zhǔn)都基于樣本相似度。但是，距離度量對(duì)于高維低樣本大?。℉DLSS）數(shù)據(jù)而言意義不大。而且，只有少量樣本的特征方差是沒有意義的，除非它能有效地表示數(shù)據(jù)分布。而不是按組查看樣本，而是根據(jù)成對(duì)方式評(píng)估樣本的效率。在我們的調(diào)查中，Wentotic認(rèn)為一次考慮一對(duì)樣本并選擇使它們靠近或遠(yuǎn)離的特征是進(jìn)行特征選擇的更好選擇。在基準(zhǔn)數(shù)據(jù)集上的實(shí)驗(yàn)結(jié)果證明了該方法在低樣本量下的有效性，該方法優(yōu)于許多其他最新特征選擇方法。

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