Boosting Naı¨ve Bayes text classification using uncertainty-based selective sampling

Kim, Han Joon; Kim, Je-Uk; Ra, Young-Gook

doi:10.1016/j.neucom.2004.09.003

Cited by 17 publications

(2 citation statements)

References 8 publications

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“…Bayesian classification is a probabilistic method [9], and it is optimal in the sense that it minimizes the expected cost of misclassification [22]. The Naïve Bayesian classifier is a simplified Bayesian classifier [5], and it has been used successfully in many applications [11,13]. Let C j , 1 6 j 6 5 denote the classes of share price trends, and L 1 denote the end time of training.…”

Section: Training the Naïve Bayesian Classifiermentioning

confidence: 99%

Supervised classification of share price trends

Zeng

Yan

2008

Information Sciences

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Section: Training the Naïve Bayesian Classifiermentioning

confidence: 99%

Supervised classification of share price trends

Zeng

Yan

2008

Information Sciences

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“…Traditional data representation, classification and retrieval problems usually focus on single modal data [3], [4]. For example, for the problem of text classification, we usually only consider using a data set of text to train a classifier [5], [6]. While for the problem of image representation, only the images are considered to learn the representation parameters [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Supervised Cross-Modal Factor Analysis for Multiple Modal Data Classification

Wang

Zhou

Duan³

et al. 2015

2015 IEEE International Conference on Systems, Man, and Cybernetics

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In this paper we study the problem of learning from multiple modal data for purpose of document classification. In this problem, each document is composed two different modals of data, i.e., an image and a text. Cross-modal factor analysis (CFA) has been proposed to project the two different modals of data to a shared data space, so that the classification of a image or a text can be performed directly in this space. A disadvantage of CFA is that it has ignored the supervision information. In this paper, we improve CFA by incorporating the supervision information to represent and classify both image and text modals of documents. We project both image and text data to a shared data space by factor analysis, and then train a class label predictor in the shared space to use the class label information. The factor analysis parameter and the predictor parameter are learned jointly by solving one single objective function. With this objective function, we minimize the distance between the projections of image and text of the same document, and the classification error of the projection measured by hinge loss function. The objective function is optimized by an alternate optimization strategy in an iterative algorithm. Experiments in two different multiple modal document data sets show the advantage of the proposed algorithm over other CFA methods.

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Secure Naïve Bayesian Classification over Encrypted Data in Cloud

Zhu

Wang

2016

Provable Security

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Boosting Naı¨ve Bayes text classification using uncertainty-based selective sampling

Cited by 17 publications

References 8 publications

Supervised classification of share price trends

Supervised classification of share price trends

Supervised Cross-Modal Factor Analysis for Multiple Modal Data Classification

Secure Naïve Bayesian Classification over Encrypted Data in Cloud

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