Combining Probabilistic Classifiers for Text Classification

Fragos, Kostas; Belsis, Petros; Skourlas, Christos

doi:10.1016/j.sbspro.2014.07.098

Cited by 8 publications

(10 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A more recent research by Fragos, Belsis and Skourlas [14] also concludes in favor of combining different approaches for text classification. The methods that authors have combined belong to same paradigm -probabilistic.…”

Section: Related Workmentioning

confidence: 98%

“…Recent research works [7,8,9,10,11,12,13,14] in the direction of combining classifiers for text classification assure that combination is always better than using individual classifiers.…”

Section: Related Workmentioning

confidence: 99%

“…The results of combinations when compared to the results of the individual classifiers are visibly better which give a boost to this area of research. The effects of combination can be very well seen in [7,8,9,10,11,12,13,14].…”

Section: Introductionmentioning

confidence: 96%

See 2 more Smart Citations

An Effective Approach for Text Classification

Mishra¹,

Modi²

2016

IJRET

View full text Add to dashboard Cite

show abstract

Section: Related Workmentioning

confidence: 98%

“…Recent research works [7,8,9,10,11,12,13,14] in the direction of combining classifiers for text classification assure that combination is always better than using individual classifiers.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An Effective Approach for Text Classification

Mishra¹,

Modi²

2016

IJRET

View full text Add to dashboard Cite

show abstract

“…In [12] combined probabilistic classifiers (Naive Bayes classifier and Maximum Entropy). They used two merging operators, max and harmonic mean.…”

Section: Related Workmentioning

confidence: 99%

Arabic text classification using master-slaves technique

Abutiheen

Aliwy

Aljanabi

2018

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Abstract. Text classification (TC) is an essential field in both text mining (TM) and natural language processing (NLP).Humans have a tendency to organize and categorize everything as they want to make things easier to understand. Therefore, text classification is an important step to achieve this goal. Arabic text classification (ATC) is a difficult process because the Arabic language has complications and limitations resulting from the nature of its morphology. In this paper, a proposed approach called the Master-Slaves technique (MST) was used to improve Arabic text classification. It consists of two main phases: in the first phase, a new Arabic corpus of 16757 text files was collected. These text files were classified into five categories manually. In the second phase, four different classifiers were implemented on the collected corpus. These classifiers are Naïve Bayes (NB), K-Nearest Neighbour (KNN), Multinomial Logistic Regression (MLR) and Maximum Weight (MW). Naïve Bayes classifier was implemented as Master and the others as Slaves. The results of these slave classifiers were used to change the probability of the Naïve Bayes classifier (Master). The four classifiers used were implemented individually and the simple voting technique was implemented among them too on the collected corpus to check the effectiveness and efficiency of the proposed technique. All the tests were applied after the pre-processing of Arabic text documents (tokenization, stemming, and stop-word removal) and each document was represented as vector of weights. For the reliability of the results, 10-fold cross-validation was used in this paper. The results showed that the Master-slaves technique gives a good improvement in accuracy of text document classification with accepted algorithm complexity compared to other techniques.

show abstract

“…In [27], Fragos K. et al combined the methods that belong to the same paradigmprobabilistic. Naive Bayes and maximum entropy classifiers are combined to test on the applications where the individual performance is good.…”

Section: Related Workmentioning

confidence: 99%

Application of the Polyhedral Conic Functions Method in the Text Classification and Comparative Analysis

Sati

Ordin

2018

Scientific Programming

View full text Add to dashboard Cite

In direct proportion to the heavy increase of online information data, the attention to text categorization (classification) has also increased. In text categorization problem, namely, text classification, the goal is to classify the documents into predefined classes (categories or labels). Recently various methods in data mining have been experienced for text classification in literature except polyhedral conic function (PCF) methods. In this paper, PCFs are used to classify the documents. The separation algorithms via PCFs which include linear programming subproblems with inequality constraints are presented. Numerical experiments are done on real-world text datasets. Comparisons are made between state-of-the-art methods by presenting obtained tenfold crossvalidation results, accuracy values, and running times in tables. The results verify that in text classification PCF methods are as effective in terms of accuracy values as state-of-the-art methods.

show abstract

Combining Probabilistic Classifiers for Text Classification

Cited by 8 publications

References 2 publications

An Effective Approach for Text Classification

An Effective Approach for Text Classification

Arabic text classification using master-slaves technique

Application of the Polyhedral Conic Functions Method in the Text Classification and Comparative Analysis

Contact Info

Product

Resources

About