An Improvement to Naive Bayes for Text Classification

Zhang, Wei; Gao, Feng

doi:10.1016/j.proeng.2011.08.404

Cited by 80 publications

(35 citation statements)

References 2 publications

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“…In the paper [5], an auxiliary feature method is proposed. It determines features by an existing feature selection method and selects an auxiliary feature which can reclassify the text space aimed at the chosen features.…”

Section: Literature Surveymentioning

confidence: 99%

Short Survey on Naive Bayes Algorithm

2017

IJAERD

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Section: Literature Surveymentioning

confidence: 99%

Short Survey on Naive Bayes Algorithm

2017

IJAERD

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“…In large database cases, data mining is the finest procedure, which extorts and identifies the required information and consequent knowledge [6]. These extort and identifications are performed with statistical, mathematical, artificial intelligence, and machine learning techniques [7]. Data Mining is capable of extorting knowledge of the e-learning systems during the testing of information with large datasets.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Educational Data Mining based ICT Competency among e-Learning Tutors using Statistical Classifier

Barik¹,

AbdulQadir²,

Difulah³

2020

IJACSA

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The implementation of computer-supported collaborative learning has come to play a pivotal role in elearning platforms. Educational Data Mining (EDM) is a promising area for the exclusive skill development of e-learning tutors, the major concern being investigations over large datasets. The tutors possessing efficient and sufficient soft skills can teach students within less time and with greater productivity. EDM is a regularly used research area that handles the development of methods to explore new ideas in the educational field. Computer-supported collaborative learning in e-learning and competencies on a real-time perspective among teachers are calculated using statistical classifiers. This paper aims to identify a feasible perspective on EDM based ICT competency over elearning tutors using statistical classifiers. A set of tutors from diverse e-learning centers of various universities is selected for the evaluation purpose. The teachers from the department of mathematics in the universities are selected to attend a professional Qualified Teacher Status numeracy skills test and tutors' online test. The results of online tests are collected and correlated with the Naive Bayes Classifiers algorithms. Naive Bayes Classifiers are used in this paper to find the classification performance results among teachers. Naive Bayes based classification is beneficial for skill identification and improvement among the teachers. Significantly, the data mining classifiers performed well with the large dataset.

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“…Data classification has a wide range of applications from financial analysis (Min & Lee, 2005;Sueyoshi, 2004;Sueyoshi & Goto, 2009), image classification (Chi, Feng, & Bruzzone, 2008;McCann & Lowe, 2012;Velasco-Forero & Angulo, 2013;Villa, Chanussot, Benediktsson, Jutten, & Dambreville, 2013), medical data for disease diagnosis or prognosis (Cascione, Ferro, Giugno, Pigola, & Pulvirenti, 2013;Dagliyan, Uney-Yuksektepe, Kavakli, & Turkay, 2011;Guyon, Weston, Barnihill, & Vapnik, 2002;Lee, Chuang, Kim, Ideker, & Lee, 2008;Seera & Lim, 2014;Su, Yoon, & Dougherty, 2009;Wei, Visweswaran, & Cooper, 2011;Yeoh et al, 2002), market price prediction (Anbazhagan & Kumarappan, 2012) and document classification (Li, Miao, & Wang, 2011;Sebastiani, 2002;Zhang & Gao, 2011).…”

Section: Introductionmentioning

confidence: 99%

Sample re-weighting hyper box classifier for multi-class data classification

Yang

Liu

Tsoka

et al. 2015

Computers & Industrial Engineering

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a b s t r a c tIn this work, we propose two novel classifiers for multi-class classification problems using mathematical programming optimisation techniques. A hyper box-based classifier (Xu & Papageorgiou, 2009) that iteratively constructs hyper boxes to enclose samples of different classes has been adopted. We firstly propose a new solution procedure that updates the sample weights during each iteration, which tweaks the model to favour those difficult samples in the next iteration and therefore achieves a better final solution. Through a number of real world data classification problems, we demonstrate that the proposed refined classifier results in consistently good classification performance, outperforming the original hyper box classifier and a number of other state-of-the-art classifiers.Furthermore, we introduce a simple data space partition method to reduce the computational cost of the proposed sample re-weighting hyper box classifier. The partition method partitions the original dataset into two disjoint regions, followed by training sample re-weighting hyper box classifier for each region respectively. Through some real world datasets, we demonstrate the data space partition method considerably reduces the computational cost while maintaining the level of prediction accuracies.

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An Improvement to Naive Bayes for Text Classification

Cited by 80 publications

References 2 publications

Short Survey on Naive Bayes Algorithm

Short Survey on Naive Bayes Algorithm

Enhancing Educational Data Mining based ICT Competency among e-Learning Tutors using Statistical Classifier

Sample re-weighting hyper box classifier for multi-class data classification

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