Effective Feature Set Selection and Centroid Classifier Algorithm for Web Services Discovery
Text preprocessing and document classification plays a vital role in web services discovery. Nearest centroid classifiers were mostly employed in high-dimensional application including genomics. Feature selection is a major problem in all classifiers and in this paper we propose to use an effective...
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2017-02-01.
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| Online Access: | Get fulltext |
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| LEADER | 02375 am a22003013u 4500 | ||
|---|---|---|---|
| 001 | ijeecs6095_6126 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a K, Venkatachalam |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a NK, Karthikeyan |e author |
| 245 | 0 | 0 | |a Effective Feature Set Selection and Centroid Classifier Algorithm for Web Services Discovery |
| 260 | |b Institute of Advanced Engineering and Science, |c 2017-02-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/6095 | ||
| 520 | |a Text preprocessing and document classification plays a vital role in web services discovery. Nearest centroid classifiers were mostly employed in high-dimensional application including genomics. Feature selection is a major problem in all classifiers and in this paper we propose to use an effective feature selection procedure followed by web services discovery through Centroid classifier algorithm. The task here in this problem statement is to effectively assign a document to one or more classes. Besides being simple and robust, the centroid classifier s not effectively used for document classification due to the computational complexity and larger memory requirements. We address these problems through dimensionality reduction and effective feature set selection before training and testing the classifier. Our preliminary experimentation and results shows that the proposed method outperforms other algorithms mentioned in the literature including K-Nearest neighbors, Naive Bayes classifier and Support Vector Machines. | ||
| 540 | |a Copyright (c) 2017 Indonesian Journal of Electrical Engineering and Computer Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc-nd/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Computer Science | ||
| 690 | |a Document processing, Centroid classifier, Ontology alignment, Semantic web, KNN algorithm, Web service annotation. | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 5, No 2: February 2017; 441-450 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v5.i2 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/6095/6126 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/6095/6126 |z Get fulltext |