Novel ways of Improving Accuracy and Performance in Ensemble Classifiers with Multiple Unbalanced Data
|International Journal of Computer Science and Engineering|
|© 2014 by SSRG - IJCSE Journal|
|Volume 1 Issue 8|
|Year of Publication : 2014|
|Authors : Praveena Prabakaran|
How to Cite?
Praveena Prabakaran, "Novel ways of Improving Accuracy and Performance in Ensemble Classifiers with Multiple Unbalanced Data," SSRG International Journal of Computer Science and Engineering , vol. 1, no. 8, pp. 1-5, 2014. Crossref, https://doi.org/10.14445/23488387/IJCSE-V1I8P103
Imbalance classification problem is considered to be one of the emergent challenges in machine learning algorithm. This problem occurs when the number of examples that represents one of the classes of the dataset is much lower than the other classes. A multi objective genetic programming approach to evolving accurate and diverse ensembles of genetic program classifiers with good performance on both the minority and majority of classes. Six benchmark binary classification problems are taken in the existing work. The main objective of the proposed work multiclass datasets are taken to improve the accuracy of minority class and two classes can be classified and each majority and minority class has specified value. The two popular Pareto-based fitness schemes in the multi objective genetic programming algorithm, SPEA2 and NSGAII can be effective in evolving a good set of non dominated solutions in some tasks, this performance needs to be improved for difficult classification problems. The importance of developing an effective fitness evaluation strategy in the underlying MOGP algorithm to evolve good ensemble members.
CLASSIFICATION, CLASS IMBALANCE LEARNING,GENETICPROGRAMMING, MULTI-OBJECTIVE MACHINE LEARNING.
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