A Novel Feature Selection Method Based on Category Distribution Ratio in Text Classification

Text classification (TC) provides a better way to organize information since it allows better understanding and interpretation of the content. It deals with the assignment of labels into a group of similar textual document. However, TC research for Asian language documents is relatively limited compared to English documents and even lesser particularly for news articles. Apart from that, TC research to classify textual documents in similar morphology such Indonesian and Malay is still scarce. Hence, the aim of this study is to develop an integrated generic TC algorithm which is able to identify the language and then classify the category for identified news documents. Furthermore, top-n feature selection method is utilized to improve TC performance and to overcome the online news corpora classification challenges: rapid data growth of online news documents, and the high computational time. Experiments were conducted using 280 Indonesian and 280 Malay online news documents from the year 2014 – 2015. The classification method is proven to produce a good result with accuracy rate of up to 95.63% for language identification, and 97.5%% for category classification. While the category classifier works optimally on n = 60%, with an average of 35 seconds computational time. This highlights that the integrated generic TC has advantage over manual classification, and is suitable for Indonesian and Malay news classification.

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A New Feature Selection Method for Text Classification Based on Independent Feature Space Search

Mathematical Problems in Engineering ◽

10.1155/2020/6076272 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14 ◽

Cited By ~ 3

Author(s):

Yong Liu ◽

Shenggen Ju ◽

Junfeng Wang ◽

Chong Su

Keyword(s):

Feature Selection ◽

Text Classification ◽

Predictive Accuracy ◽

Feature Selection Method ◽

Feature Space ◽

Selection Method ◽

The Other ◽

Feature Subset ◽

Search Range ◽

Text Documents

Feature selection method is designed to select the representative feature subsets from the original feature set by different evaluation of feature relevance, which focuses on reducing the dimension of the features while maintaining the predictive accuracy of a classifier. In this study, we propose a feature selection method for text classification based on independent feature space search. Firstly, a relative document-term frequency difference (RDTFD) method is proposed to divide the features in all text documents into two independent feature sets according to the features’ ability to discriminate the positive and negative samples, which has two important functions: one is to improve the high class correlation of the features and reduce the correlation between the features and the other is to reduce the search range of feature space and maintain appropriate feature redundancy. Secondly, the feature search strategy is used to search the optimal feature subset in independent feature space, which can improve the performance of text classification. Finally, we evaluate several experiments conduced on six benchmark corpora, the experimental results show the RDTFD method based on independent feature space search is more robust than the other feature selection methods.

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A NEW FEATURE SELECTION METHOD FOR TEXT CLASSIFICATION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001407005466 ◽

2007 ◽

Vol 21 (02) ◽

pp. 423-438 ◽

Cited By ~ 9

Author(s):

GULDEN UCHYIGIT ◽

KEITH CLARK

Keyword(s):

Feature Selection ◽

Text Classification ◽

Information Gain ◽

Feature Selection Method ◽

Feature Space ◽

Selection Method ◽

Computational Time ◽

Small Subset ◽

Selection Methods ◽

New Feature

Text classification is the problem of classifying a set of documents into a pre-defined set of classes. A major problem with text classification problems is the high dimensionality of the feature space. Only a small subset of these words are feature words which can be used in determining a document's class, while the rest adds noise and can make the results unreliable and significantly increase computational time. A common approach in dealing with this problem is feature selection where the number of words in the feature space are significantly reduced. In this paper we present the experiments of a comparative study of feature selection methods used for text classification. Ten feature selection methods were evaluated in this study including the new feature selection method, called the GU metric. The other feature selection methods evaluated in this study are: Chi-Squared (χ2) statistic, NGL coefficient, GSS coefficient, Mutual Information, Information Gain, Odds Ratio, Term Frequency, Fisher Criterion, BSS/WSS coefficient. The experimental evaluations show that the GU metric obtained the best F1 and F2 scores. The experiments were performed on the 20 Newsgroups data sets with the Naive Bayesian Probabilistic Classifier.

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A mutual information and information entropy pair based feature selection method in text classification

2010 International Conference on Computer Application and System Modeling (ICCASM 2010) ◽

10.1109/iccasm.2010.5620805 ◽

2010 ◽

Author(s):

Zhili Pei ◽

Yuxin Zhou ◽

Lisha Liu ◽

Lihua Wang ◽

Yinan Lu ◽

...

Keyword(s):

Feature Selection ◽

Mutual Information ◽

Text Classification ◽

Information Entropy ◽

Feature Selection Method ◽

Selection Method ◽

Entropy Pair

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Distance Variance Score: An Efficient Feature Selection Method in Text Classification

Mathematical Problems in Engineering ◽

10.1155/2015/695720 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Heyong Wang ◽

Ming Hong

Keyword(s):

Feature Selection ◽

Text Mining ◽

Text Classification ◽

Web Applications ◽

Rapid Development ◽

Feature Selection Method ◽

Selection Method ◽

Text Documents ◽

Unsupervised Feature Selection ◽

Classification Feature

With the rapid development of web applications such as social network, a large amount of electric text data is accumulated and available on the Internet, which causes increasing interests in text mining. Text classification is one of the most important subfields of text mining. In fact, text documents are often represented as a high-dimensional sparse document term matrix (DTM) before classification. Feature selection is essential and vital for text classification due to high dimensionality and sparsity of DTM. An efficient feature selection method is capable of both reducing dimensions of DTM and selecting discriminative features for text classification. Laplacian Score (LS) is one of the unsupervised feature selection methods and it has been successfully used in areas such as face recognition. However, LS is unable to select discriminative features for text classification and to effectively reduce the sparsity of DTM. To improve it, this paper proposes an unsupervised feature selection method named Distance Variance Score (DVS). DVS uses feature distance contribution (a ratio) to rank the importance of features for text documents so as to select discriminative features. Experimental results indicate that DVS is able to select discriminative features and reduce the sparsity of DTM. Thus, it is much more efficient than LS.

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A novel probabilistic feature selection method for text classification

Knowledge-Based Systems ◽

10.1016/j.knosys.2012.06.005 ◽

2012 ◽

Vol 36 ◽

pp. 226-235 ◽

Cited By ~ 145

Author(s):

Alper Kursat Uysal ◽

Serkan Gunal

Keyword(s):

Feature Selection ◽

Text Classification ◽

Feature Selection Method ◽

Selection Method

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English Text Classification Using Improved Recursive Feature Elimination (IRFE) Algorithm: تصنيف النص الإنجليزي باستخدام الخوارزمية العودية المحسنة لإزالة الخواص (IRFE)

Journal of engineering sciences and information technology - مجلة العلوم الهندسية و تكنولوجيا المعلومات ◽

10.26389/ajsrp.r080420 ◽

2020 ◽

Vol 4 (2) ◽

Author(s):

Esraa H. Abd Al-Ameer, Ahmed H. Aliwy

Keyword(s):

Feature Selection ◽

Language Processing ◽

Text Classification ◽

Feature Selection Method ◽

Selection Method ◽

English Text ◽

Recursive Feature Elimination ◽

Chi Square ◽

Data Set ◽

New Feature

Documents classification is from most important fields for Natural language processing and text mining. There are many algorithms can be used for this task. In this paper, focuses on improving Text Classification by feature selection. This means determine some of the original features without affecting the accuracy of the work, where our work is a new feature selection method was suggested which can be a general formulation and mathematical model of Recursive Feature Elimination (RFE). The used method was compared with other two well-known feature selection methods: Chi-square and threshold. The results proved that the new method is comparable with the other methods, The best results were 83% when 60% of features used, 82% when 40% of features used, and 82% when 20% of features used. The tests were done with the Naïve Bayes (NB) and decision tree (DT) classification algorithms , where the used dataset is a well-known English data set “20 newsgroups text” consists of approximately 18846 files. The results showed that our suggested feature selection method is comparable with standard Like Chi-square.

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