Centroid Estimation Based on Symmetric KL Divergence for Multinomial Text Classification Problem

Chen, Jiangning; Matzinger, Heinrich; Zhai, Haoyan; Zhou, Mi

doi:10.1109/icmla.2018.00189

Cited by 12 publications

(9 citation statements)

References 7 publications

(6 reference statements)

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“…For example, we can treat each class as a multinomial distribution, and the corresponding documents are samples generated by the distribution. With this assumption, we desire to find the centroid for every class, by either using the maximum likelihood function or defining other different objective functions [2] in both supervised and unsupervised learning version [7]. Although the assumption of this method is not exact in this task, Naive Bayes achieves high accuracy in practical problems.…”

Section: Related Workmentioning

confidence: 99%

A Cost-Reducing Partial Labeling Estimator in Text Classification Problem

Chen

Dai

Duan

et al. 2020

Advances in Intelligent Systems and Computing

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We propose a new approach to address the text classification problems when learning with partial labels is beneficial. Instead of offering each training sample a set of candidate labels, we assign negativeoriented labels to the ambiguous training examples if they are unlikely fall into certain classes. We construct our new maximum likelihood estimators with self-correction property, and prove that under some conditions, our estimators converge faster. Also we discuss the advantages of applying one of our estimator to a fully supervised learning problem. The proposed method has potential applicability in many areas, such as crowd-sourcing, natural language processing and medical image analysis.

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Section: Related Workmentioning

confidence: 99%

A Cost-Reducing Partial Labeling Estimator in Text Classification Problem

Chen

Dai

Duan

et al. 2020

Advances in Intelligent Systems and Computing

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“…KL divergence is an asymmetric metric, however, the symmetric version of the KL divergence is often used [18], [19].…”

Section: B Kullback-leibler Divergencementioning

confidence: 99%

Hierarchical Clustering Based Band Selection Algorithm for Hyperspectral Face Recognition

et al. 2019

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Hyperspectral face recognition is a small sample size problem, where usually less than four hyperspectral cubes are available as training data. At the same time, hyperspectral face image acquires grayscale images over a series of continuous spectra which usually contain large redundant information or noise, especially in the near infrared spectrum bands. Therefore, dimensionality reduction and feature extraction are important tasks on this problem. This paper proposes a hierarchical clustering-based spectrum band selection method, which mitigates the influence of noise and extracts features from each spectra band by using the Gabor filter and the histograms of oriented gradients algorithm, In addition, the fusion of Hog and Gabor features was embedded into the nearest neighborhood-based classifier for performance comparison. The experimental results show that the proposed algorithm is time effective and provides robust performance.INDEX TERMS Hyperspectral face recognition, band selection, Gabor filter, HOG features, image fusion.

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“…1a, b, we see the testing error is only decreasing slightly as t increasing from 0.1 to 2. We summarize this fact as follows 3 We take 10 largest groups in Reuter-21578 dataset (a) and 20 news group dataset (b), and take 90% of the data as training set. The y-axis is the accuracy, and the x-axis is the class index Proposition 6.1 For prediction purpose, the correlation factor t can take value in the interval…”

Section: Robustness Of T For Predictionmentioning

confidence: 99%

“…There are some researches on how to relax this restriction, such as the feature weighting approach [12,33] and instanceweighting approach [32]. [3] proposed a method that finds better estimation of centroid, which helps improve the accuracy of Naive Bayes estimation. In order to tackle the situation where there does not exist enough labelled data for each class, we propose a novel estimation method.…”

Section: Introductionmentioning

confidence: 99%

Improved Naive Bayes with optimal correlation factor for text classification

et al. 2019

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Naive Bayes (NB) estimator is widely-used in text classification problems. However, it does not perform well with smallsize training datasets. Most previous literature focuses on either creating and modifying features or combing clustering to improve the performance of NB. We directly tackle the problem by constructing a new estimator, called Naive Bayes with correlation factor. We introduce a correlation factor to NB estimator that incorporates overall correlation among the different classes. This effectively exploits the idea of bootstrapping, which reuses data for all classes even if they only belong to one class. Moreover, we obtain a formula for the optimal correlation factor by balancing bias and variance of the estimator. Experimental results on real-world data show that our estimator achieves better accuracy compared with traditional Naive Bayes, yet at the same time maintaining the simplicity of NB.

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Centroid Estimation Based on Symmetric KL Divergence for Multinomial Text Classification Problem

Cited by 12 publications

References 7 publications

A Cost-Reducing Partial Labeling Estimator in Text Classification Problem

A Cost-Reducing Partial Labeling Estimator in Text Classification Problem

Hierarchical Clustering Based Band Selection Algorithm for Hyperspectral Face Recognition

Improved Naive Bayes with optimal correlation factor for text classification

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