Fuzzy Systems

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Comprehensive Feature Selection for Clinical Dataset


Affiliations
1 Sri Ramakrishna College of Arts and Science College for Women, Coimbatore - 641044, India
     

   Subscribe/Renew Journal


Feature selection plays a significant role in any data mining research problem. In this research work, comprehensive feature selection is applied for selecting the attributes in the chosen PIMA Indian diabetes dataset. The comprehensive feature selection mechanism makes use of maximum significance pattern for selecting the most edifying features, which effectively distinguish between different classes of samples.

Keywords

Feature Selection, Data Mining, Gestational Diabetes, Accuracy, Time Taken, Feature Selection, Risk Prediction.
User
Subscription Login to verify subscription
Notifications
Font Size

  • A. Konar, Computational Intelligence: Principles, Techniques and Applications, Springer-Verlag, 2005, pp. 24.

  • O. Cordon, F. Herrera, F. Hoffmann, L. Magdalena, Genetic Fuzzy Systems, Evo- lutionary Tuning and Learning of Fuzzy Knowledge Bases, World Scientific, 2001.

  • D. Wu, W.W. Tan, Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers, Eng. Appl. Artificial Intell. 19 (2006) 829–841.

  • M. Last, S. Eyal, A fuzzy-based lifetime extension of genetic algorithms, Fuzzy Sets Syst. 149 (2005) 131–147.

  • H. Wang, S. Kwong, Y. Jin, W. Wei, K.F. Man, Multi-objective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction, Fuzzy Sets Syst. 149 (2005) 149–186.

  • S.K. Oh, W. Pedrycz, H.S. Park, Rule-based multi-FNN identification with the aid of evolutionary fuzzy granulation, Knowledge-Based Syst. 17 (2004) 1–13.

  • P.P. Angelov, R.A. Buswell, Automatic generation of fuzzy rule-based models from data by genetic algorithms, Inform. Sci. 150 (2003) 17–31.

  • K.M. Chow, A.B. Rad, On-line fuzzy identification using genetic algorithms, Fuzzy Sets Syst. 132 (2002) 147–171.

  • I. Jagielska, C. Matthews, T. Whitfort, An investigation into the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition for classification problems, Neuro Comput. 24 (1999) 37–54.

  • M. Russo, FuGeNeSys—A fuzzy genetic neural system for fuzzy modeling, IEEE Trans. Fuzzy Syst. 6 (1998) 373–388.

  • R. Alcala, J. Alcala-Fdez, F. Herrera, A proposal for the genetic lateral tuning of linguistic fuzzy systems and its interaction with rule selection, IEEE Trans. Fuzzy Syst. 15 (4) (2007) 616–635.

  • J. Casillas, B. Carse, L. Bull, Fuzzy-XCS: A Michigan Genetic Fuzzy System, IEEE Trans. Fuzzy Syst. 15 (4) (2007) 536–550.

  • L. Sanchez, I. Couso, Advocating the use of imprecisely observed data in genetic fuzzy systems, IEEE Trans. Fuzzy Syst. 15 (4) (2007) 551–562.


Abstract Views: 259

PDF Views: 2




  • Comprehensive Feature Selection for Clinical Dataset

Abstract Views: 259  |  PDF Views: 2

Authors

S. Kavipriya
Sri Ramakrishna College of Arts and Science College for Women, Coimbatore - 641044, India
T. Deepa
Sri Ramakrishna College of Arts and Science College for Women, Coimbatore - 641044, India

Abstract


Feature selection plays a significant role in any data mining research problem. In this research work, comprehensive feature selection is applied for selecting the attributes in the chosen PIMA Indian diabetes dataset. The comprehensive feature selection mechanism makes use of maximum significance pattern for selecting the most edifying features, which effectively distinguish between different classes of samples.

Keywords


Feature Selection, Data Mining, Gestational Diabetes, Accuracy, Time Taken, Feature Selection, Risk Prediction.

References