A novel probabilistic feature selection method for text classification

Uysal, Alper Kürşat; Günal, Serkan

doi:10.1016/j.knosys.2012.06.005

Cited by 266 publications

(139 citation statements)

References 38 publications

(45 reference statements)

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“…Uysal and Gunal [10] developed a probabilistic feature selection method. The method aims to assign a high score to the distinctive terms, which frequently occur in a particular class and do not occur in the other classes.…”

Section: Related Workmentioning

confidence: 99%

A feature selection model based on genetic rank aggregation for text sentiment classification

Onan

Korukoğlu

2016

Journal of Information Science

328

167

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Sentiment analysis is an important research direction of natural language processing, text mining and web mining which aims to extract subjective information in source materials. The main challenge encountered in machine learning method-based sentiment classification is the abundant amount of data available. This amount makes it difficult to train the learning algorithms in a feasible time and degrades the classification accuracy of the built model. Hence, feature selection becomes an essential task in developing robust and efficient classification models whilst reducing the training time. In text mining applications, individual filter-based feature selection methods have been widely utilized owing to their simplicity and relatively high performance. This paper presents an ensemble approach for feature selection, which aggregates the several individual feature lists obtained by the different feature selection methods so that a more robust and efficient feature subset can be obtained. In order to aggregate the individual feature lists, a genetic algorithm has been utilized. Experimental evaluations indicated that the proposed aggregation model is an efficient method and it outperforms individual filter-based feature selection methods on sentiment classification.

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Section: Related Workmentioning

confidence: 99%

A feature selection model based on genetic rank aggregation for text sentiment classification

Onan

Korukoğlu

2016

Journal of Information Science

328

167

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“…After this process, any classifier can implement the text classification process by predicting the label of the document. The research community working in this field is still studying how to improve the performance of text classification by combining various preprocessing [43,46], feature extraction [47], feature selection [42,48], and ensemble methods [49]. The following features are extracted for the proposed model:…”

Section: Feature Extraction and Selectionmentioning

confidence: 99%

“…Refs. [42,43] suggest that feature selection is a very important stage in addition to feature extraction and classification. The selected data are moved to the preprocessing module in order to transform data to suit the learning algorithms, ultimately resulting in quality output.…”

Section: Preprocessingmentioning

confidence: 99%

Prognosis Essay Scoring and Article Relevancy Using Multi-Text Features and Machine Learning

Mehmood

Lee

et al. 2017

Symmetry

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Abstract:This study develops a model for essay scoring and article relevancy. Essay scoring is a costly process when we consider the time spent by an evaluator. It may lead to inequalities of the effort by various evaluators to apply the same evaluation criteria. Bibliometric research uses the evaluation criteria to find relevancy of articles instead. Researchers mostly face relevancy issues while searching articles. Therefore, they classify the articles manually. However, manual classification is burdensome due to time needed for evaluation. The proposed model performs automatic essay evaluation using multi-text features and ensemble machine learning. The proposed method is implemented in two data sets: a Kaggle short answer data set for essay scoring that includes four ranges of disciplines (Science, Biology, English, and English language Arts), and a bibliometric data set having IoT (Internet of Things) and non-IoT classes. The efficacy of the model is measured against the Tandalla and AutoP approach using Cohen's kappa. The model achieves kappa values of 0.80 and 0.83 for the first and second data sets, respectively. Kappa values show that the proposed model has better performance than those of earlier approaches.

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“…Statistic CHI (X 2 statistic) specifies the independence lack between two random variables (D c and ti) [38,40].…”

Section: Mutual Informationmentioning

confidence: 99%

A Novel Statistical Feature Selection Approach for Text Categorization

Fattah¹

2017

J Inf Process Syst

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For text categorization task, distinctive text features selection is important due to feature space high dimensionality. It is important to decrease the feature space dimension to decrease processing time and increase accuracy. In the current study, for text categorization task, we introduce a novel statistical feature selection approach. This approach measures the term distribution in all collection documents, the term distribution in a certain category and the term distribution in a certain class relative to other classes. The proposed method results show its superiority over the traditional feature selection methods.

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A novel probabilistic feature selection method for text classification

Cited by 266 publications

References 38 publications

A feature selection model based on genetic rank aggregation for text sentiment classification

A feature selection model based on genetic rank aggregation for text sentiment classification

Prognosis Essay Scoring and Article Relevancy Using Multi-Text Features and Machine Learning

A Novel Statistical Feature Selection Approach for Text Categorization

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