کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
515534 867038 2013 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A generalized cluster centroid based classifier for text categorization
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
A generalized cluster centroid based classifier for text categorization
چکیده انگلیسی

In this paper, a Generalized Cluster Centroid based Classifier (GCCC) and its variants for text categorization are proposed by utilizing a clustering algorithm to integrate two well-known classifiers, i.e., the K-nearest-neighbor (KNN) classifier and the Rocchio classifier. KNN, a lazy learning method, suffers from inefficiency in online categorization while achieving remarkable effectiveness. Rocchio, which has efficient categorization performance, fails to obtain an expressive categorization model due to its inherent linear separability assumption. Our proposed method mainly focuses on two points: one point is that we use a clustering algorithm to strengthen the expressiveness of the Rocchio model; another one is that we employ the improved Rocchio model to speed up the categorization process of KNN. Extensive experiments conducted on both English and Chinese corpora show that GCCC and its variants have better categorization ability than some state-of-the-art classifiers, i.e., Rocchio, KNN and Support Vector Machine (SVM).


► We focus on using a constrained clustering algorithm to integrate the KNN and Rocchio classifiers.
► Clustering can be used to strengthen the Rocchio model.
► The KNN categorization process can be greatly accelerated by using the improved Rocchio model together with the KNN decision rule to perform categorization.
► Extensive experiments on heterogeneous corpora show the effectiveness and efficiency of our proposed method.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Processing & Management - Volume 49, Issue 2, March 2013, Pages 576–586
نویسندگان
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