A Semisupervised Cascade Classification Algorithm
Joint Authors
Karlos, Stamatis
Fazakis, Nikos
Kotsiantis, Sotiris
Sgarbas, Kyriakos
Source
Applied Computational Intelligence and Soft Computing
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-02-22
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Information Technology and Computer Science
Abstract EN
Classification is one of the most important tasks of data mining techniques, which have been adopted by several modern applications.
The shortage of enough labeled data in the majority of these applications has shifted the interest towards using semisupervised methods.
Under such schemes, the use of collected unlabeled data combined with a clearly smaller set of labeled examples leads to similar or even better classification accuracy against supervised algorithms, which use labeled examples exclusively during the training phase.
A novel approach for increasing semisupervised classification using Cascade Classifier technique is presented in this paper.
The main characteristic of Cascade Classifier strategy is the use of a base classifier for increasing the feature space by adding either the predicted class or the probability class distribution of the initial data.
The classifier of the second level is supplied with the new dataset and extracts the decision for each instance.
In this work, a self-trained NB ∇ C4.5 classifier algorithm is presented, which combines the characteristics of Naive Bayes as a base classifier and the speed of C4.5 for final classification.
We performed an in-depth comparison with other well-known semisupervised classification methods on standard benchmark datasets and we finally reached to the point that the presented technique has better accuracy in most cases.
American Psychological Association (APA)
Karlos, Stamatis& Fazakis, Nikos& Kotsiantis, Sotiris& Sgarbas, Kyriakos. 2016. A Semisupervised Cascade Classification Algorithm. Applied Computational Intelligence and Soft Computing،Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1094908
Modern Language Association (MLA)
Karlos, Stamatis…[et al.]. A Semisupervised Cascade Classification Algorithm. Applied Computational Intelligence and Soft Computing No. 2016 (2016), pp.1-14.
https://search.emarefa.net/detail/BIM-1094908
American Medical Association (AMA)
Karlos, Stamatis& Fazakis, Nikos& Kotsiantis, Sotiris& Sgarbas, Kyriakos. A Semisupervised Cascade Classification Algorithm. Applied Computational Intelligence and Soft Computing. 2016. Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1094908
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1094908