Weight evaluation for features via constrained data-pairscan’t-linkq

Liu, Ming; Wu, Chong; Liu, Yuanchao

doi:10.1016/j.ins.2014.05.029

Cited by 1 publication

(2 citation statements)

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“…The CHI statistic is widely used in text classification as well as in other machine learning applications, which measures the independence between the random variable B and C , and is given by Li et al proposed a supervised feature selection method, named CHIR, which is based on the χ 2 statistic and new statistical data that can measure the positive term-category dependency [26]. …”

Section: Related Workmentioning

confidence: 99%

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Relevance popularity: A term event model based feature selection scheme for text classification

Feng

Yang

et al. 2017

PLoS ONE

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Feature selection is a practical approach for improving the performance of text classification methods by optimizing the feature subsets input to classifiers. In traditional feature selection methods such as information gain and chi-square, the number of documents that contain a particular term (i.e. the document frequency) is often used. However, the frequency of a given term appearing in each document has not been fully investigated, even though it is a promising feature to produce accurate classifications. In this paper, we propose a new feature selection scheme based on a term event Multinomial naive Bayes probabilistic model. According to the model assumptions, the matching score function, which is based on the prediction probability ratio, can be factorized. Finally, we derive a feature selection measurement for each term after replacing inner parameters by their estimators. On a benchmark English text datasets (20 Newsgroups) and a Chinese text dataset (MPH-20), our numerical experiment results obtained from using two widely used text classifiers (naive Bayes and support vector machine) demonstrate that our method outperformed the representative feature selection methods.

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Section: Related Workmentioning

confidence: 99%

“…Term frequency information has gained much more attention in term weighing processes [21–25]. To accurately assign feature’s weight, Liu et al in [26], proposed a novel constraint based weight evaluation using constrained data-pairs. These methods often contain a local weight factor and a global weight factor.…”

Section: Introductionmentioning

confidence: 99%

Relevance popularity: A term event model based feature selection scheme for text classification

Feng

Yang

et al. 2017

PLoS ONE

View full text Add to dashboard Cite

show abstract

Weight evaluation for features via constrained data-pairscan’t-linkq

Cited by 1 publication

References 53 publications

Relevance popularity: A term event model based feature selection scheme for text classification

Relevance popularity: A term event model based feature selection scheme for text classification

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