The week's pick
Random Articles
Reseach Article
Multinovel Class Detection in the Data Stream Classification by using SVM
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 119 - Number 19 |
| Year of Publication: 2015 |
| Authors: Chaitrali Chavan, Vinod Wadne |
10.5120/21172-3950
|
Chaitrali Chavan, Vinod Wadne . Multinovel Class Detection in the Data Stream Classification by using SVM. International Journal of Computer Applications. 119, 19 ( June 2015), 1-3. DOI=10.5120/21172-3950
Abstract
There are many challenges which community faces In Data Mining, concerning with the data stream categorization. The four different issue of categorization viz. infinite length, concept drift, concept, development feature, development. Due to infinite length of data, it is impossible to store and use the traditional data. Many researchers focus on the issues of all of the four challenges for data stream categorization. In this system novel class are detected by using the Gini coefficient method and outliers are detected by using the adaptive threshold method. We used SVM method for detecting the multi novel class detection. In the present system data are divided into fixed sized of chunks for classifying the stream instances, because of this system fail to capture the concept drift immediately. That's why solution of this method to the change point detection method which are trying to determine the chunk size dynamically on the data stream. The computational complexity is improved by clustering algorithm the performance is checked of the system by using the forest outlier dataset.
References
- M. M. Masud, Q. Chen, J. Gao, L. Khan, J. Han, and B. M. Thuraisingham, "Classification and Novel Class Detection of Data Streams in a Dynamic Feature Space," Proc. European Conf. Machine Learning and
- M. M. Masud, J. Gao, L. Khan, J. Han, and B. M. Thuraisingham, "Integrating Novel Class Detection with Classification for Concept- Drifting Data Streams," Proc. European Conf. Machine Learning and Knowledge Discovery in Databases (ECML PKDD), pp. 79-94, 2009.
- Charu C. Aggarwal "A Framework for Classification and Segmentation of Massive Audio Data Streams"
- J. Kolter and M. Maloof, "Using Additive Expert Ensembles to Cope with Concept Drift," Proc. 22nd Intl Conf. Machine Learning (ICML), pp. 449-456, 2005.
- M. M. Masud, Q. Chen, L. Khan, C. Aggarwal, J. Gao, J. Han, and B. M. Thuraisingham, "Addressing Concept-Evolution in Concept- Drifting DataStreams," Proc. IEEE Intl Conf. Data Mining (ICDM), pp. 929- 934, 2010. .
- A. Bifet, G. Holmes, B. Pfahringer, R. Kirkby, and R. Gavalda, "NewEnsemble Methods for Evolving Data Streams," Proc. ACM SIGKDD 15thIntl Conf. Knowledge Discovery and Data Mining, pp. 139-148, 2009
- H. Wang, W. Fan, P. S. Yu, and J. Han. Mining concept drifting data streams using ensemble classifiers. In SIGKDD, pages 226-235, 2003.
- S. Hashemi, Y. Yang, Z. Mirzamomen, and M. Kangavari, "Adapted Oneversus- All Decision Trees for Data Stream Classification," IEEE Trans. Knowledge and Data Eng. , vol. 21, no. 5, pp. 624-637, May 2009.
- W. N Street and Y. Kim. A Straming assembling Algorithm for large scale classification. In processing of the 7th ACM SIGKDD international Conference knowledge of Discovery and data Mining
Index Terms
Keywords