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Abstract
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Article Information:
Text Categorization using Reduced Training Set
Mohamed Goudjil, Mouloud Koudil, Mouldi Bedda and Noureddine Ghoggali
Corresponding Author: Mohamed Goudjil
Submitted: February 26, 2015
Accepted: March 25, 2015
Published: August 25, 2015 |
Abstract:
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The machine learning approaches to text categorization proceed by teaching the system how to classify through labeled samples. In real application scenarios, the collection of training (labeled) samples to design a classifier is not always trivial due to the complexity and the cost which characterize the process. A possible solution to this issue can be found in the exploitation of the large number of unlabeled samples which are accessible at zero cost from the web. Active learning strives to reduce the required labeling effort while retaining the accuracy by intelligently selecting the samples to be labeled. This Study presents a novel active learning method for text classification that selects a batch of informative samples for manual labeling by an expert. The proposed method uses the posterior probability output of a multi-class SVM method. The experiments are performed with two well-known datasets and the presented experimental results show that employing our active learning method can significantly reduce the need for labeled training data.
Key words: Active learning, pairwise coupling, pool-based active learning, support vector machine, text classification, ,
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Cite this Reference:
Mohamed Goudjil, Mouloud Koudil, Mouldi Bedda and Noureddine Ghoggali, . Text Categorization using Reduced Training Set. Research Journal of Applied Sciences, Engineering and Technology, (12): 1363-1369.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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