A Novel Active Learning Method Using SVM for Text Classification

作     者：Mohamed Goudjil Mouloud Koudil Mouldi Bedda Noureddine Ghoggali 

作者机构：Ecole nationale Superieure d'Informatique(ESI)Oued SmarAlgiersAlgeria AL Jouf UniversitySakakaKingdom of Saudi Arabia LAAAS laboratoryFaculte de TechnologieUniversite Batna 2FesdisAlgeria 

出 版 物：《International Journal of Automation and computing》 (国际自动化与计算杂志（英文版）)

年 卷 期：2018年第15卷第3期

页      面：290-298页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work is supported by the Air Force Geophysical Laboratory and the Office of Naval Research 

主　　题：Text categorization active learning support vector machine (SVM) pool-based active learning pairwise coupling. 

摘      要：Support vector machines（SVMs） are a popular class of supervised learning algorithms, and are particularly applicable to large and high-dimensional classification problems. Like most machine learning methods for data classification and information retrieval, they require manually labeled data samples in the training stage. However, manual labeling is a time consuming and errorprone task. One possible solution to this issue is to exploit the large number of unlabeled samples that are easily accessible via the internet. This paper presents a novel active learning method for text categorization. The main objective of active learning is to reduce the labeling effort, without compromising the accuracy of classification, by intelligently selecting which samples should be *** proposed method selects a batch of informative samples using the posterior probabilities provided by a set of multi-class SVM classifiers, and these samples are then manually labeled by an expert. Experimental results indicate that the proposed active learning method significantly reduces the labeling effort, while simultaneously enhancing the classification accuracy.