کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
406476 678086 2014 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Impact of a metric of association between two variables on performance of filters for binary data
ترجمه فارسی عنوان
تأثیر یک متریک ارتباط بین دو متغیر در عملکرد فیلترها برای داده های باینری
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

In the feature selection community, filters are quite popular. Design of a filter depends on two parameters, namely the objective function and the metric it employs for estimating the feature-to-class (relevance) and feature-to-feature (redundancy) association. Filter designers pay relatively more attention towards the objective function. But a poor metric can overshadow the goodness of an objective function. The metrics that have been proposed in the literature estimate the relevance and redundancy differently, thus raising the question: can the metric estimating the association between two variables improve the feature selection capability of a given objective function or in other words a filter. This paper investigates this question. Mutual information is the metric proposed for measuring the relevance and redundancy between the features for the mRMR filter [1] while the MBF filter [2] employs correlation coefficient. Symmetrical uncertainty, a variant of mutual information, is used by the fast correlation-based filter (FCBF) [3]. We carry out experiments on mRMR, MBF and FCBF filters with three different metrics (mutual information, correlation coefficient and diff-criterion) using three binary data sets and four widely used classifiers. We find that MBF׳s performance is much better if it uses diff-criterion rather than correlation coefficient while mRMR with diff-criterion demonstrates performance better or comparable to mRMR with mutual information. For the FCBF filter, the diff-criterion also exhibits results much better than mutual information.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 143, 2 November 2014, Pages 248–260
نویسندگان
, , ,