دانلود مقاله ISI انگلیسی شماره 46161
عنوان فارسی مقاله

ادغام خوشه بندی نمودار با بهینه سازی کلونی مورچه ها برای انتخاب ویژگی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
46161 2015 18 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Integration of graph clustering with ant colony optimization for feature selection
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Knowledge-Based Systems, Volume 84, August 2015, Pages 144–161

کلمات کلیدی
انتخاب ویژگی - بهینه سازی کلونی مورچه - روش فیلتر - خوشه بندی نمودار
پیش نمایش مقاله
پیش نمایش مقاله ادغام خوشه بندی نمودار با بهینه سازی کلونی مورچه ها برای انتخاب ویژگی

چکیده انگلیسی

Feature selection is an important preprocessing step in machine learning and pattern recognition. The ultimate goal of feature selection is to select a feature subset from the original feature set to increase the performance of learning algorithms. In this paper a novel feature selection method based on the graph clustering approach and ant colony optimization is proposed for classification problems. The proposed method’s algorithm works in three steps. In the first step, the entire feature set is represented as a graph. In the second step, the features are divided into several clusters using a community detection algorithm and finally in the third step, a novel search strategy based on the ant colony optimization is developed to select the final subset of features. Moreover the selected subset of each ant is evaluated using a supervised filter based method called novel separability index. Thus the proposed method does not need any learning model and can be classified as a filter based feature selection method. The proposed method integrates the community detection algorithm with a modified ant colony based search process for the feature selection problem. Furthermore, the sizes of the constructed subsets of each ant and also size of the final feature subset are determined automatically. The performance of the proposed method has been compared to those of the state-of-the-art filter and wrapper based feature selection methods on ten benchmark classification problems. The results show that our method has produced consistently better classification accuracies.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.