کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4950799 | 1441033 | 2018 | 9 صفحه PDF | دانلود رایگان |
- We generate cluster-dependent feature weights reflecting the relevance of features.
- Features with a relatively low weight are removed from a data set.
- Our methods outperform other popular alternatives in synthetic and real-world data.
Feature selection is a popular data pre-processing step. The aim is to remove some of the features in a data set with minimum information loss, leading to a number of benefits including faster running time and easier data visualisation. In this paper we introduce two unsupervised feature selection algorithms. These make use of a cluster-dependent feature-weighting mechanism reflecting the within-cluster degree of relevance of a given feature. Those features with a relatively low weight are removed from the data set. We compare our algorithms to two other popular alternatives using a number of experiments on both synthetic and real-world data sets, with and without added noisy features. These experiments demonstrate our algorithms clearly outperform the alternatives.
Journal: Information Processing Letters - Volume 129, January 2018, Pages 44-52