کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4946470 | 1439291 | 2016 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Binary coordinate ascent: An efficient optimization technique for feature subset selection for machine learning
ترجمه فارسی عنوان
صعود مختصات دودویی: تکنیک بهینه سازی کارآمد برای انتخاب زیر مجموعه های ویژگی برای یادگیری ماشین
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
فراگیری ماشین، طبقه بندی، انتخاب ویژگی، بسته بندی بهینه سازی، ابتکاری،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Feature subset selection (FSS) has been an active area of research in machine learning. A number of techniques have been developed for selecting an optimal or sub-optimal subset of features, because it is a major factor to determine the performance of a machine-learning technique. In this paper, we propose and develop a novel optimization technique, namely, a binary coordinate ascent (BCA) algorithm that is an iterative deterministic local optimization that can be coupled with wrapper or filter FSS. The algorithm searches throughout the space of binary coded input variables by iteratively optimizing the objective function in each dimension at a time. We investigated our BCA approach in wrapper-based FSS under area under the receiver-operating-characteristic (ROC) curve (AUC) criterion for the best subset of features in classification. We evaluated our BCA-based FSS in optimization of features for support vector machine, multilayer perceptron, and Naïve Bayes classifiers with 12 datasets. Our experimental datasets are distinct in terms of the number of attributes (ranging from 18 to 11,340), and the number of classes (binary or multi-class classification). The efficiency in terms of the number of subset evaluations was improved substantially (by factors of 5-37) compared with two popular FSS meta-heuristics, i.e., sequential forward selection (SFS) and sequential floating forward selection (SFFS), while the classification performance for unseen data was maintained.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 110, 15 October 2016, Pages 191-201
Journal: Knowledge-Based Systems - Volume 110, 15 October 2016, Pages 191-201
نویسندگان
Amin Zarshenas, Kenji Suzuki,