COMPARATIVE ANALYSIS OF PARTICLE SWARM OPTIMIZATION ALGORITHMS FOR TEXT FEATURE SELECTION

With the rapid growth of Internet, more and more natural language text documents are available in electronic format, making automated text categorization a must in most fields. Due to the high dimensionality of text categorization tasks, feature selection is needed before executing document classification. There are basically two kinds of feature selection approaches: the filter approach and the wrapper approach. For the wrapper approach, a search algorithm for feature subsets and an evaluation algorithm for assessing the fitness of the selected feature subset are required. In this work, I focus on the comparison between two wrapper approaches. These two approaches use Particle Swarm Optimization (PSO) as the search algorithm. The first algorithm is PSO based K-Nearest Neighbors (KNN) algorithm, while the second is PSO based Rocchio algorithm. Three datasets are used in this study. The result shows that BPSO-KNN is slightly better in classification results than BPSO-Rocchio, while BPSO-Rocchio has far shorter computation time than BPSO-KNN

New techniques for Arabic document classification

Text classification (TC) concerns automatically assigning a class (category) label to a text document, and has increasingly many applications, particularly in the domain of organizing, for browsing in large document collections. It is typically achieved via machine learning, where a model is built on the basis of a typically large collection of document features. Feature selection is critical in this process, since there are typically several thousand potential features (distinct words or terms). In text classification, feature selection aims to improve the computational e ciency and classification accuracy by removing irrelevant and redundant terms (features), while retaining features (words) that contain su cient information that help with the classification task. This thesis proposes binary particle swarm optimization (BPSO) hybridized with either K Nearest Neighbour (KNN) or Support Vector Machines (SVM) for feature selection in Arabic text classi cation tasks. Comparison between feature selection approaches is done on the basis of using the selected features in conjunction with SVM, Decision Trees (C4.5), and Naive Bayes (NB), to classify a hold out test set. Using publically available Arabic datasets, results show that BPSO/KNN and BPSO/SVM techniques are promising in this domain. The sets of selected features (words) are also analyzed to consider the di erences between the types of features that BPSO/KNN and BPSO/SVM tend to choose. This leads to speculation concerning the appropriate feature selection strategy, based on the relationship between the classes in the document categorization task at hand. The thesis also investigates the use of statistically extracted phrases of length two as terms in Arabic text classi cation. In comparison with Bag of Words text representation, results show that using phrases alone as terms in Arabic TC task decreases the classification accuracy of Arabic TC classifiers significantly while combining bag of words and phrase based representations may increase the classification accuracy of the SVM classifier slightly

Toward Optimal Feature Selection in Naive Bayes for Text Categorization

Automated feature selection is important for text categorization to reduce the feature size and to speed up the learning process of classifiers. In this paper, we present a novel and efficient feature selection framework based on the Information Theory, which aims to rank the features with their discriminative capacity for classification. We first revisit two information measures: Kullback-Leibler divergence and Jeffreys divergence for binary hypothesis testing, and analyze their asymptotic properties relating to type I and type II errors of a Bayesian classifier. We then introduce a new divergence measure, called Jeffreys-Multi-Hypothesis (JMH) divergence, to measure multi-distribution divergence for multi-class classification. Based on the JMH-divergence, we develop two efficient feature selection methods, termed maximum discrimination ($MD$) and $MD-\chi^2$ methods, for text categorization. The promising results of extensive experiments demonstrate the effectiveness of the proposed approaches.Comment: This paper has been submitted to the IEEE Trans. Knowledge and Data Engineering. 14 pages, 5 figure

FSMJ: Feature Selection with Maximum Jensen-Shannon Divergence for Text Categorization

In this paper, we present a new wrapper feature selection approach based on Jensen-Shannon (JS) divergence, termed feature selection with maximum JS-divergence (FSMJ), for text categorization. Unlike most existing feature selection approaches, the proposed FSMJ approach is based on real-valued features which provide more information for discrimination than binary-valued features used in conventional approaches. We show that the FSMJ is a greedy approach and the JS-divergence monotonically increases when more features are selected. We conduct several experiments on real-life data sets, compared with the state-of-the-art feature selection approaches for text categorization. The superior performance of the proposed FSMJ approach demonstrates its effectiveness and further indicates its wide potential applications on data mining.Comment: 8 pages, 6 figures, World Congress on Intelligent Control and Automation, 201

EEF: Exponentially Embedded Families with Class-Specific Features for Classification

In this letter, we present a novel exponentially embedded families (EEF) based classification method, in which the probability density function (PDF) on raw data is estimated from the PDF on features. With the PDF construction, we show that class-specific features can be used in the proposed classification method, instead of a common feature subset for all classes as used in conventional approaches. We apply the proposed EEF classifier for text categorization as a case study and derive an optimal Bayesian classification rule with class-specific feature selection based on the Information Gain (IG) score. The promising performance on real-life data sets demonstrates the effectiveness of the proposed approach and indicates its wide potential applications.Comment: 9 pages, 3 figures, to be published in IEEE Signal Processing Letter. IEEE Signal Processing Letter, 201

Non-Standard Words as Features for Text Categorization

This paper presents categorization of Croatian texts using Non-Standard Words (NSW) as features. Non-Standard Words are: numbers, dates, acronyms, abbreviations, currency, etc. NSWs in Croatian language are determined according to Croatian NSW taxonomy. For the purpose of this research, 390 text documents were collected and formed the SKIPEZ collection with 6 classes: official, literary, informative, popular, educational and scientific. Text categorization experiment was conducted on three different representations of the SKIPEZ collection: in the first representation, the frequencies of NSWs are used as features; in the second representation, the statistic measures of NSWs (variance, coefficient of variation, standard deviation, etc.) are used as features; while the third representation combines the first two feature sets. Naive Bayes, CN2, C4.5, kNN, Classification Trees and Random Forest algorithms were used in text categorization experiments. The best categorization results are achieved using the first feature set (NSW frequencies) with the categorization accuracy of 87%. This suggests that the NSWs should be considered as features in highly inflectional languages, such as Croatian. NSW based features reduce the dimensionality of the feature space without standard lemmatization procedures, and therefore the bag-of-NSWs should be considered for further Croatian texts categorization experiments.Comment: IEEE 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2014), pp. 1415-1419, 201