Rajashree Shettar

Work place: Rashtreeya Vidyalaya College of Engineering, Bengaluru, India



Research Interests: Data Mining


Dr. Rajashree Shettar is currently working as Professor, Dept of Computer Science, RV College of Engineering, Bengaluru. Her research work focuses on Knowledge Discovery in Semi-structured Data. She has around 45 publications in various International Journals and Conferences. She has authored a book on Sequential Pattern Mining from Web Log Data: Concepts, Techniques and Applications of Web Usage Mining, published by LAMBERT Academic Publishingcompany, Germany and co-authored book chapters.She has published Indian Patent titled Developing a Therapeutic Biomarker for Ebola Viral Disease along with Dr. Vidya Niranjan, Sanchit Mittal and Nishka Ranjan

Author Articles
Ferrer diagram based partitioning technique to decision tree using genetic algorithm

By Pavan Sai Diwakar Nutheti Narayan Hasyagar Rajashree Shettar Shankru Guggari Umadevi V

DOI: https://doi.org/10.5815/ijmsc.2020.01.03, Pub. Date: 8 Feb. 2020

Decision tree is a known classification technique in machine learning. It is easy to understand and interpret and widely used in known real world applications. Decision tree (DT) faces several challenges such as class imbalance, overfitting and curse of dimensionality. Current study addresses curse of dimensionality problem using partitioning technique. It uses partitioning technique, where features are divided into multiple sets and assigned into each block based on mutual exclusive property. It uses Genetic algorithm to select the features and assign the features into each block based on the ferrer diagram to build multiple CART decision tree. Majority voting technique used to combine the predicted class from the each classifier and produce the major class as output. The novelty of the method is evaluated   with 4 datasets from UCI repository and shows approximately 9%, 3% and 5% improvement as compared with CART, Bagging and Adaboost techniques.

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