A Technique for Improving the Performance of Naive Bayes Text Classification

Jiang, Yuqian; Lin, Hung‐Mo; Wang, Xuesong; Lu, Dongming

doi:10.1007/978-3-642-23982-3_25

Cited by 11 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of NB, Zhu et al [ 12 ] used the NB algorithm for text classification. Jiang et al [ 41 ] proposed an improved NB technology for text classification performance. This method solves the problem of unsatisfactory results caused by the uneven distribution of training data.…”

Section: Literature Reviewmentioning

confidence: 99%

Short-Text Classification Detector: A Bert-Based Mental Approach

Ding

Dou

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

With the continuous development of the Internet, social media based on short text has become popular. However, the sparsity and shortness of essays will restrict the accuracy of text classification. Therefore, based on the Bert model, we capture the mental feature of reviewers and apply them for short text classification to improve its classification accuracy. Specifically, we construct a model text at the language level and fine tune the model to better embed mental features. To verify the accuracy of this method, we compare a variety of machine learning methods, such as support vector machine, convolution neural networks, and recurrent neural networks. The results show the following: (1) Through feature comparison, it is found that mental features can significantly improve the accuracy of short text classification. (2) Combining mental features and text as input vectors can provide more classification accuracy than separating them as two independent vectors. (3) Through model comparison, it can be found that Bert model can integrate mental features and short text. Bert can better capture mental features to improve the accuracy of classification results. This will help to promote the development of short text classification.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Short-Text Classification Detector: A Bert-Based Mental Approach

Ding

Dou

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…An objective of the experiment is that the better the results obtained using the same categorization method-the better the representation is. Very popular approach to text categorization is the Naive Bayes classifier, e.g., Jiang et al (2011). We use this widely studied approach to perform classification of the text within 10 test data packages.…”

Section: Evaluation Of Combined Representationmentioning

confidence: 99%

Spectral Clustering Wikipedia Keyword-Based Search Results

Szymański

Dziubich

2017

Front. Robot. AI

View full text Add to dashboard Cite

The paper summarizes our research in the area of unsupervised categorization of Wikipedia articles. As a practical result of our research, we present an application of spectral clustering algorithm used for grouping Wikipedia search results. The main contribution of the paper is a representation method for Wikipedia articles that has been based on combination of words and links and used for categoriation of search results in this repository. We evaluate the proposed approach with Primary Component projections and show, on the test data, how usage of cosine transformation to create combined representations influence data variability. On sample test datasets, we also show how combined representation improves the data separation that increases overall results of data categorization. To implement the system, we review the main spectral clustering methods and we test their usability for text categorization. We give a brief description of the system architecture that groups online Wikipedia articles retrieved with user-specified keywords. Using the system, we show how clustering increases information retrieval effectiveness for Wikipedia data repository.

show abstract

“…Up to now, different algorithms have been used to diagnose various diseases. In the present study, the researchers proposed a new model for the diagnosis of heart disease based on the Farmland Fertility Algorithm (FFA) [12] and NB [13]. It used the BFFA for FS and the NB algorithm for samples classification.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Binary Farmland Fertility Algorithm with Naïve Bayes for Diagnosis of Heart Disease

RADPOUR

Gharehchopogh

2022

Sakarya University Journal of Computer and Information Sciences

View full text Add to dashboard Cite

One of the essential aims of intelligent algorithms concerning the diagnosis of heart disease is to achieve accurate results and discover valuable patterns. This paper proposes a new hybrid model based on Binary Farmland Fertility Algorithm (BFFA) and Naïve Bayes (NB) to diagnose heart disease. The BFFA is used for Feature Selection (FS) and the NB for data classification. FS can be employed to discover the most beneficial features. Four valid and universal UCI datasets (Hearts, Cleveland, Hungary, and Switzerland) were used to diagnose heart disease. Each dataset included 13 main features. The evaluation of the proposed model is simulated in MATLAB 2017b. The number of features in four datasets of Heart, Cleveland, Hungary, and Switzerland is equal to 13, which was reduced to six for each dataset through the BFFA to better the efficiency of the proposed model. For evaluation, the accuracy criterion, the criterion of accuracy in the proposed model for all features in the four datasets, Heart, Cleveland, Hungary, and Switzerland, is equal to 82.25%, 86.91%, 89.32%, and 89.24%, respectively. Results of the proposed model showed appropriateness in comparison to some other methods. In this paper, the proposed model was compared with other methods, and it was found that the proposed model possessed a better accuracy percentage.

show abstract

A Technique for Improving the Performance of Naive Bayes Text Classification

Cited by 11 publications

References 8 publications

Short-Text Classification Detector: A Bert-Based Mental Approach

Short-Text Classification Detector: A Bert-Based Mental Approach

Spectral Clustering Wikipedia Keyword-Based Search Results

A Novel Hybrid Binary Farmland Fertility Algorithm with Naïve Bayes for Diagnosis of Heart Disease

Contact Info

Product

Resources

About