Ensemble of Diverse Decorate Classifier with Neural Network |
Author(s): |
| Sweety Patel , Amrut vahini college of engineering; Deepali Kaul, kautilya institute of technology and engineering; Anuradha Nawathe, kautilya institute of technology and engineering; Sunny Patel, K.K.W, Nashik |
Keywords: |
| Artificial neural network, Classification, Classifier, DECORATE Ensemble, Diversity, Neural Network Ensembles, UCI Datasets |
Abstract |
|
Challenging problem in data mining is to classify data sets that suffer from imbalanced Class distributions. In machine learning, the ensemble of classifiers are known to increase the accuracy of single classifiers by combining all of them, but none of these learning techniques alone solve the imbalance problem, to deal with this problem the ensemble algorithms have to be designed specifically . DECORATE is ensemble learning techniques, that directly constructs diverse hypotheses using additional artificially-constructed training examples. ANN is very flexible with respect to missing, incomplete and noisy data and also makes the data to use for dynamic environment. In this paper, we present a method of generating the diversity of the classifier data set from UCI repository by neural networks and learning by DECORATE for optimum accuracy. |
Other Details |
|
Paper ID: IJSRDV2I4184 Published in: Volume : 2, Issue : 4 Publication Date: 01/07/2014 Page(s): 965-968 |
Article Preview |
|
|
|
|
