Efficient Pre-Processing Techniques for Improving Classifiers Performance

Authors

  • S. Nickolas Department of Computer Applications, National Institute of Technology, Tiruchirappalli, Tamilnadu 620015, India
  • K. Shobha High Performance Computing Lab, Department of Computer Applications, National Institute of Technology, Tiruchirappalli, Tamilnadu 620015, India https://orcid.org/0000-0002-6208-2705

DOI:

https://doi.org/10.13052/jwe1540-9589.2124

Keywords:

data Mining, data pre-processing, decision trees, Expectation Maximization (EM) algorithms, neural networks.

Abstract

Data pre-processing plays a vital role in the life cycle of data mining for accomplishing quality outcomes. In this paper, it is experimentally shown the importance of data pre-processing to achieve highly accurate classifier outcomes by imputing missing values using a novel imputation method, CLUSTPRO, by selecting highly correlated features using Correlation-based Variable Selection (CVS) and by handling imbalanced data using Synthetic Minority Over-sampling Technique (SMOTE). The proposed CLUSTPRO method makes use of Random Forest (RF) and Expectation Maximization (EM) algorithms to impute missing. The imputed results are evaluated using standard evaluation metrics. The CLUSTPRO imputation method outperforms existing, state-of-the-art imputation methods. The combined approach of imputation, feature selection, and imbalanced data handling techniques has significantly contributed to attaining an improved classification accuracy (AUC curve) of 40%–50% in comparison with results obtained without any pre-processing.

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Author Biographies

S. Nickolas, Department of Computer Applications, National Institute of Technology, Tiruchirappalli, Tamilnadu 620015, India

S. Nickolas is a Professor in the Department of Computer Applications, National Institute of Technology, Tiruchirappalli, Tamilnadu, India. He received his M.E. Computer Science from REC, Trichy in 1992 and Ph.D in the year 2007 from NIT, Trichy. He is the Professor In-Charge of the Massively Parallel Programming Laboratory, NVIDIA CUDA Teaching Centre, NIT, Trichy. His research interest includes Evolutionary Algorithms, Data Mining, Big Data Analytics, Distributed Computing, Cloud Computing and Software Metrics.

K. Shobha, High Performance Computing Lab, Department of Computer Applications, National Institute of Technology, Tiruchirappalli, Tamilnadu 620015, India

K. Shobha is a Research Scholar in the Department of Computer Applications, National Institute of Technology, Tiruchirappalli, Tamilnadu, India. Her research interest Data Mining, Big Data Analytics, Cloud Computing and Software Metrics, Computer Networks.

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Published

2021-12-30

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Section

Articles