An Elaboration of Text Categorization and Automatic Text Classification Through Mathematical and Graphical Modelling

Faraz, Ahmed

doi:10.5121/cseij.2015.5301

Cited by 9 publications

(7 citation statements)

References 8 publications

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“…At the beginning of the classification, it is necessary to select the document that will be carried out for classification and include it in the belong category [9]. For the selected document, its weight value also must be determined by Chi Square method.…”

Section: K-nearest Neighbourmentioning

confidence: 99%

Machine Learning Algorithms for Myanmar News Classification

Nwet¹

2019

IJNLC

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Text classification is a very important research area in machine learning. Artificial Intelligence is reshaping text classification techniques to better acquire knowledge. In spite of the growth and spread of AI in text mining research for various languages such as English, Japanese, Chinese, etc., its role with respect to Myanmar text is not well understood yet. The aim of this paper is comparative study of machine learning algorithms such as Naïve Bayes (NB), k-nearest neighbours (KNN), support vector machine (SVM) algorithms for Myanmar Language News classification. There is no comparative study of machine learning algorithms in Myanmar News. The news is classified into one of four categories (political, Business, Entertainment and Sport). Dataset is collected from 12,000 documents belongs to 4 categories. Well-known algorithms are applied on collected Myanmar language News dataset from websites. The goal of text classification is to classify documents into a certain number of pre-defined categories. News corpus is used for training and testing purpose of the classifier. Feature selection method, chi square algorithm achieves comparable performance across a number of classifiers. In this paper, the experimental results also show support vector machine is better accuracy to other classification algorithms employed in this research. Due to Myanmar Language is complex, it is more important to study and understand the nature of data before proceeding into mining.

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Section: K-nearest Neighbourmentioning

confidence: 99%

Machine Learning Algorithms for Myanmar News Classification

Nwet¹

2019

IJNLC

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“…We have been given a predefined [12] can be applied to the data which is in the form of natural language text. The natural language text is divided or categorized among subset of texts and labelled according to the theme which is the main idea or subject.…”

Section: Text Categorization In Data Miningmentioning

confidence: 99%

Improved Nearest Neighbour Approach for Document Categorization

Wadhawan¹,

Mittal²

2017

IJERMT

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“…Text classification or documents classification is the problem of assigning unlabeled text instances to one or more predefined labelled classes or categories [4] [5][6] [7]. Text classification has been utilized in various application domains [8], e.g.…”

Section: Introductionmentioning

confidence: 99%

A New Feature Filtering Approach by Integrating IG and T-Test Evaluation Metrics for Text Classification

Ado¹,

Deris²,

Samsudin³

2021

IJACSA

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High dimensionality is one of the main issues associated with text classification, such as selecting the most discrepant features subset for classifier's effective utilization is a difficult task. This significant preprocessing stage of selecting the relevant features is often called feature selection or feature filtering. Eliminating the non-relevant and noise features from the original feature set will drastically reduce the size of the feature set and the time complexity of the classification models and also improve or maintain their performance. Most of the existing filtering method produced a subset with relatively high number of features without much significant impact on running time, or produced subset with lesser number of features but results in performance degradation. In this paper, we proposed a new bi-strategy filtering approach that integrates Information Gain with t-test that selects a subset of informative features by considering both the score and ranking of respective features. Our approach considers the results' disparity produced by the benchmark metrics used in order to maximized and lessen their advantage and disadvantage. The approach set a new threshold parameter by computing V-score of the features with minimum scores present in both the two subsets and further refined the selected features. Hence, it reduces the size of the features subset without losing much informative features. Experiment results conducted on three different text datasets have shown that the proposed method is able to select features that are highly discrepant and at the same time achieves a significant improvement in terms of classification accuracy and F-score at the cost of a minimum running time.

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An Elaboration of Text Categorization and Automatic Text Classification Through Mathematical and Graphical Modelling

Cited by 9 publications

References 8 publications

Machine Learning Algorithms for Myanmar News Classification

Machine Learning Algorithms for Myanmar News Classification

Improved Nearest Neighbour Approach for Document Categorization

A New Feature Filtering Approach by Integrating IG and T-Test Evaluation Metrics for Text Classification

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