A Semisupervised Cascade Classification Algorithm
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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2016/5919717 |
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| author | Stamatis Karlos Nikos Fazakis Sotiris Kotsiantis Kyriakos Sgarbas |
| author_facet | Stamatis Karlos Nikos Fazakis Sotiris Kotsiantis Kyriakos Sgarbas |
| author_sort | Stamatis Karlos |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5bb745ad138348fe94023a8e538d28b2 |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-5bb745ad138348fe94023a8e538d28b22025-02-03T01:21:30ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322016-01-01201610.1155/2016/59197175919717A Semisupervised Cascade Classification AlgorithmStamatis Karlos0Nikos Fazakis1Sotiris Kotsiantis2Kyriakos Sgarbas3Department of Mathematics, University of Patras, 26504 Rio, GreeceDepartment of Electrical and Computer Engineering, University of Patras, 26504 Rio, GreeceDepartment of Mathematics, University of Patras, 26504 Rio, GreeceDepartment of Electrical and Computer Engineering, University of Patras, 26504 Rio, GreeceClassification 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.http://dx.doi.org/10.1155/2016/5919717 |
| spellingShingle | Stamatis Karlos Nikos Fazakis Sotiris Kotsiantis Kyriakos Sgarbas A Semisupervised Cascade Classification Algorithm Applied Computational Intelligence and Soft Computing |
| title | A Semisupervised Cascade Classification Algorithm |
| title_full | A Semisupervised Cascade Classification Algorithm |
| title_fullStr | A Semisupervised Cascade Classification Algorithm |
| title_full_unstemmed | A Semisupervised Cascade Classification Algorithm |
| title_short | A Semisupervised Cascade Classification Algorithm |
| title_sort | semisupervised cascade classification algorithm |
| url | http://dx.doi.org/10.1155/2016/5919717 |
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