Shankru Guggari

Work place: B.M.S College of Engineering, Bengaluru, India



Research Interests: Data Structures and Algorithms, Pattern Recognition, Computational Learning Theory


Mr. Shankru Guggari (Ph.D), M.Tech., Research scholar, in the Dept. of Computer science and Engineering, B.M.S. College of Engineering, Bangalore. He is currently working in classification technique area for his Ph.D dissertation. Recently, he has won the best research paper award in the international conference. Pattern recognition, IOT and Machine learning are the interested research area of him. He has published some of his research works in international conferences and a research paper in Elsevier publication journal. He has more than 4 years of industry experience and more than 3 years in academic research experience.

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:, 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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