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A Selective Data on Performance Feature with Selective Algorithms

  • M. BharatEmail author
  • Konda Raveendra
  • Y. Ravi Kumar
  • K. Santhi Sree
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 8)

Abstract

Now a days, there is rapid development is Computer Science and Engineering Technology as well as data has been increasing wildly and it is a major problem for users to quickly find the most relevant (or useful) information or data from large amount of data being stored in databases. To solve this problem so many researchers are found that the feature selection algorithms (or methods) are best methods to identify to useful data (or information) from large amount of data present in databases. Feature Selection algorithm is useful in identifying (or selecting) most useful information from the entire large original set of features to improve accuracy. Feature Selection specifies a task that selects a subset of features and those are useful to solve domain problems. There are several important algorithms (or methods) are available for selecting the relevant features from large set of features to improve accuracy of the results. This paper explains performance of various feature selection algorithms to find most relevant features from the entire set of features.

Keywords

Feature selection Performance of feature selection Data mining Relief 

References

  1. 1.
    Artificial Intelligence Foundations Theory and Algorithms, 2015.Google Scholar
  2. 2.
    Naidu, Kajal, Aparna Dhenge, and Kapil Wankhade. “Feature Selection Algorithm for Improving the Performance of Classification: A Survey”, 2014 Fourth International Conference on Communication Systems and Network Technologies, 2014.Google Scholar
  3. 3.
    Zhang Yu; Yu Gang; Guan Yongsheng and Yang Donghui. “Feature Selection of Nonperforming Loans in Chinese Commercial Banks”, International Journal of U- & EService, Science & Technology, 2015.Google Scholar
  4. 4.
    Chin, Ang, Andri Mirzal, Habibollah Haron, and Haza Hamed. “Supervised, Unsupervised and Semisupervised Feature selection: A Review on Gene Selection”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2015.Google Scholar
  5. 5.
    Sharma, Poonam, Abhisek Mathur, and Sushil Chaturvedi. “An improved fast clustering-based feature subset selection algorithm for multi featured dataset”, 2014 International Conference on Advances in Engineering & Technology Research (ICAETR - 2014), 2014.Google Scholar
  6. 6.
    Nemade, Rachana T., and Richa Makhijani. “Unsupervised feature selection for linked data”, International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014), 2014.Google Scholar
  7. 7.
    Wang, Guangtao, Qinbao Song, Baowen Xu, and Yuming Zhou. “Selecting feature subset for high dimensional data via the propositional FOIL rules”, Pattern Recognition, 2013.Google Scholar
  8. 8.
    Khalid, Samina, Tehmina Khalil, and Shamila Nasreen. “A survey of feature selection and feature extraction techniques in machine learning”, 2014 Science and Information Conference, 2014.Google Scholar
  9. 9.
    Shang Lei. “A Feature Selection Method Based on Information Gain and Genetic Algorithm”, 2012 International Conference on Computer Science and Electronics Engineering, 03/2012.Google Scholar
  10. 10.
    Zhao, Zhou, Xiaofei He, Lijun Zhang, Wilfred Ng, and Yueting Zhuang. “Graph Regularized Feature Selection with Data Reconstruction”, IEEE Transactions on Knowledge and Data Engineering, 2015.Google Scholar
  11. 11.
    de L. Vieira, Davi C., Paulo J. L. Adeodato, and Paulo M. Goncalves. “Improving reinforcement learning algorithms by the use of data mining techniques for feature and action selection”, 2010 IEEE International Conference on Systems Man and Cybernetics, 2010.Google Scholar
  12. 12.
    “A Lexicographic Multi-Objective Genetic Algorithm for Multi-Label Correlation Based Feature Selection”, Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference GECCO Companion 15, 2015.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • M. Bharat
    • 1
    Email author
  • Konda Raveendra
    • 1
  • Y. Ravi Kumar
    • 1
  • K. Santhi Sree
    • 2
  1. 1.Department of CSEHyderabadIndia
  2. 2.Department of CSEJawaharlal Nehru Technological University HyderabadHyderabadIndia

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