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2004
Springer

A selective sampling approach to active feature selection

9 years 10 months ago
A selective sampling approach to active feature selection
Feature selection, as a preprocessing step to machine learning, has been very effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving result comprehensibility. Traditional feature selection methods resort to random sampling in dealing with data sets with a huge number of instances. In this paper, we introduce the concept of active feature selection, and investigate a selective sampling approach to active feature selection in a filter model setting. We present a formalism of selective sampling based on data variance, and apply it to a widely used feature selection algorithm Relief. Further, we show how it realizes active feature selection and reduces the required number of training instances to achieve time savings without performance deterioration. We design objective evaluation measures of performance, conduct extensive experiments using both synthetic and benchmark data sets, and observe consistent and significant improvement. We s...
Huan Liu, Hiroshi Motoda, Lei Yu
Added 16 Dec 2010
Updated 16 Dec 2010
Type Journal
Year 2004
Where AI
Authors Huan Liu, Hiroshi Motoda, Lei Yu
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