Feature Selection Using Association Word Mining for Classification

Ko, Su-Jeong; Lee, Jung-Hyun

doi:10.1007/3-540-44759-8_22

Cited by 12 publications

(1 citation statement)

References 8 publications

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“…Many feature evaluation metrics have been notable among which are information gain (IG), term frequency, Chi-square, expected cross entropy, Odds Ratio, the weight of evidence of text, mutual information, Gini index. But FS of association word mining is more efficient than IG and document frequency [57] .Other various methods are presented like [58] sampling method which is randomly samples roughly features and then make matrix for classification. By considering problem of high dimensional problem [59] is presented new FS witch use the genetic algorithm (GA) optimization.…”

Section: Feature Selectionmentioning

confidence: 99%

Text Classification and Classifiers:A Survey

Korde¹,

Mahender²

2012

IJAIA

184

106

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As most information (over 80%) is stored as text, text mining is believed to have a high commercial potential value. knowledge may be discovered from many sources of information; yet, unstructured texts remain the largest readily available source of knowledge .Text classification which classifies the documents according to predefined categories .In this paper we are tried to give the introduction of text classification, process of text classification as well as the overview of the classifiers and tried to compare the some existing classifier on basis of few criteria like time complexity, principal and performance.

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Section: Feature Selectionmentioning

confidence: 99%