A selective sampling approach to active feature selection

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 sele...

Active Sampling for Feature Selection

In many knowledge discovery applications the data mining step is followed by further data acquisition. New data may consist of new instances and/or new features for the old instances. When new features are to be added an acquisition policy can help decide what features have to be acquired based on their predictive capability and the cost of acquisition. This can be posed as a feature selection ...

A Novel Approach to Feature Selection Using PageRank algorithm for Web Page Classification

In this paper, a novel filter-based approach is proposed using the PageRank algorithm to select the optimal subset of features as well as to compute their weights for web page classification. To evaluate the proposed approach multiple experiments are performed using accuracy score as the main criterion on four different datasets, namely WebKB, Reuters-R8, Reuters-R52, and 20NewsGroups. By analy...

Active Sample Learning and Feature Selection: A Unified Approach

This paper focuses on the problem of simultaneous sample and feature selection for machine learning in a fully unsupervised setting. Though most existing works tackle these two problems separately that derives two well-studied subareas namely active learning and feature selection, a unified approach is inspirational since they are often interleaved with each other. Noisy and high-dimensional fe...

A Graph-Based Approach to Feature Selection