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Efficient web data classification techniques using semi-supervise learning algorithm

Published: 23 January 2012 Publication History

Abstract

In Organization the data is very important that increase the volume of information that is available on the web and that leads to the design of efficient and accurate web data classification systems. In this paper, we define a framework to improve the performance of a base classifier, by clustering the unlabeled data with labeled data using clustering algorithm (training of samples) labeling of clusters (majority voting for each Hyperspheres) and final generated classified data. We have used construction of BNN based semi-supervised classifier while training and testing of the classifier is performed. We have studied and customized a supervised classification algorithm to form out semi-supervised classification that leads to design a multiclass semi-supervised classifier using geometrical expansion. The experimental result shows provision for the classifier designer followed by training and testing medical disease dataset using pre-decided samples. Our classification model consists of training phase that covers two process clustering and labeling to perform classification task of medical data and the binary neural network is trained. In this we used two techniques normalization and quantization for pre-processing the datasets. Pre-processing impart various outcomes after applying the classification model like number of hypersphere, confusing samples that cannot be learned, training time and label of hypersphere. Comparison has been done for implementation and design of Binary Neural Network Classifier Algorithm with the other existing traditional algorithms. Our classifier evaluates performance in terms of generalization, number of hidden neuron and accuracy etc. The BNN-CA construct three-layered binary neural network (BNN) and can solve any semi-labeled multi-class problem.

References

[1]
Di Wanga and Narendra. S. Chaudhari, "A fast modified constructive-covering algorithm for binary multi-layer neural networks," School of Informatics, The University of Manchester, Sackvill Street, M601QH Manchester, UK, School of Computer Engineering, Nanyang Technological University (NTU), Singapore,
[2]
Di Wang, Narendra S. Chaudhari and Jagdish Chandra Patra, "Fast Constructive-Covering Approach for Neural Networks," School of Computer Engineering, Nanyang Technological University, Singapore,
[3]
Girish Keswani and Lawrence 0. Hall," Text Classification with Enhanced Semi-supervised Fuzzy Clustering" 0-7803-7280-8/02/$10.00 02,2002 IEEE.
[4]
C. Campbell, "Constructive learning techniques for designing neural network systems," in C. T. Leondes (Ed.), Neural Network Systems, Techniques and applications, Academic press, San Diego, 1997, pp. 1--54,
[5]
D. Wang and N. S. Choudhari, "A novel training algorithm for Boolean neural networks based on multi level geometrical expansion," Neurocomputing 57C (2004) 455--461.
[6]
J. H. Kim and S. K. Park, "The geometrical learning of binary neural neworks," IEEE Transaction. Neural Networks 6(1995) 237--247,
[7]
T. Y. Kwok and D. Y. Yeung, "Constructive learning for structure learning in feed-forword neural networks for regression problems," IEEE Transaction, Neural Networks 8(1997) 630--645,
[8]
Dan Zhang, Jingdong Wang, Fei Wang and Changshui Zhang, "Semi-Supervised Classification with Universum," State Key Laboratory on Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing, 100084,
[9]
Di Wanga and Narendra. S. Chaudhari, "A constructive unsupervised learning algorithm for clustering binary patterns," in: Proceeding of international joint conference on Neural Networks 2004, vol.4, Budapest, Hungary, 2004, pp. 1381--1386,
[10]
Jaffrey Erman, Anirban Mahanti, Martin Arlit, Ira Cohen, Carey Williamson, "Semi-Supervised Network Traffic Classification," Department of Computer Science, University of Calgary, Department of Computer Science and Engineering, Indian Institute of Technology (Delhi), Delhi,
[11]
Balaji Krishnapuram and DavidWilliams, "On Semi-Supervised Classification," Ya Xue, Alex Hartemink, Lawrence CarinDuke University, USA,
[12]
Jelena and John R Smith, "Semantic Labeling of Multimedia Content," IBM Watson Research Center, 19 Skyline Drive, Hawthorne, NY 10532.
[13]
Kevin Y. Yip, Michael K. Ng, David W. Cheung, "Input Validation for Semi-supervised Clustering," Department of Computer Scienc, Yale University, New Haven, USA, Department of Mathematics, Baptist University, Department of Computer Science University of Hong Kong. Hong Kong,
[14]
Daewon Lee and Jaewook Lee, "Equilibrium-Based Support Vector Machine for Semi-supervised Classification," IEEE Transaction On Neural Network, VOL. 18, No. 2, March 2007.

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COMPUTE '12: Proceedings of the 5th ACM COMPUTE Conference: Intelligent & scalable system technologies
January 2012
146 pages
ISBN:9781450314404
DOI:10.1145/2459118
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 23 January 2012

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Author Tags

  1. BNN-CA
  2. data classification techniques
  3. data pre-processing
  4. semi-supervise learning
  5. supervise learning
  6. unsupervised learning and BNN

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Compute '12
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  • ACM Pune Professional Chapter

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COMPUTE '12 Paper Acceptance Rate 18 of 116 submissions, 16%;
Overall Acceptance Rate 114 of 622 submissions, 18%

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  • (2021)Reformist Framework for Improving Human Security for Mobile Robots in Industry 4.0Mobile Information Systems10.1155/2021/47442202021Online publication date: 1-Jan-2021

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