AN APPROPRIATE FEATURE CLASSIFICATION MODEL USING KOHONEN NETWORK
Journal: International Journal of Computer Engineering and Technology (IJCET) (Vol.10, No. 2)Publication Date: 2019-03-18
Authors : R. Sridevi P. Dinadayalan S. Bastin Britto;
Page : 148-159
Keywords : Classification; Cloud database; Recurrent Neural Network; SelfOrganizing Map;
Abstract
Self-Organizing Maps are widely used unsupervised neural network architecture to discover group of structures in a dataset. Feature Selection plays a major role in Machine Learning. “An Appropriate Feature Classification Model using Kohonen Network (AFCM)” is based on Recurrent Neural Network approach for feature selection which clusters relevant and irrelevant features from the dataset present in cloud environment. The proposed model not only clusters relevant and irrelevant features but also refine the clustering process by minimizing the errors and irrelevant features. The AFCM consists of Feature Selection Organizer and Convergence SOM. In the Feature Selection Organizer, features are clusters into Relevant and Irrelevant Feature classes. The Convergence SOM helps to improve the prediction accuracy in the Relevant Feature set and to reduce the irrelevant features. The efficiency of the proposed model is extensively tested upon real world medical datasets. The experimental result on standard medical dataset shows that the AFCM is better than the Traditional models.
Other Latest Articles
- SECURE DATA TRANSMISSION THROUGH NODE-DISJOINT ON DEMAND MULTIPATH ROUTING IN MANETS
- A case of an iatrogenic vesicourethral fistula following an anastomotic urethroplasty with progressive perineal approach for a posterior urethral distraction defect
- AN APPROACH FOR PREDICATION OF COMPLICATIONS WOMEN FIGHTS DURING MATERNAL PERIOD
- ANALYSIS OF SOCIAL MEDIA TEXTUAL CONTENT USING ACCIDENT DATA SETS FOR CONTEXT RECOGNITION BY GENETIC ALGORITHM
- Carcinoma prostate with testicular secondaries A rare presentation
Last modified: 2019-05-07 18:42:27