Programs
- B. Sc. Clinical Nutrition, Dietetics, and Food Science - Undergraduate
- Post Doctoral Fellowship in EEG & Clinical Epileptology - Fellowship
Publication Type : Conference Paper
Publisher : 2020 International Conference on Communication and Signal Processing (ICCSP), IEEE
Source : 2020 International Conference on Communication and Signal Processing (ICCSP), IEEE, Chennai, India (2020)
Url : https://ieeexplore.ieee.org/document/9182109
Campus : Amritapuri
School : Department of Computer Science and Engineering, School of Engineering
Department : Computer Science
Year : 2020
Abstract : In this 21st century the world is driven by data, analysis, and predictions based on this data is substantial. However, these predictions that have an immense impact on our daily life comes with an overhead of complex data mining and large datasets. With this paper, we will suggest a way to reduce the dimensionality of the dataset without a great loss of accuracy and reduce the necessity for complex data mining, by analyzing the features based on their SHAP - SHapley Additive explanation, values we prioritize the features and discard the features of unsubstantial relevance to the accuracy of the model.
Cite this Research Publication : Chejarla Santosh Kumar, Movva Naga Suman Choudary, Vinay Babu Bommineni, Grandhi Tarun, and Anjali T., “Dimensionality Reduction based on SHAP Analysis: A Simple and Trustworthy Approach”, in 2020 International Conference on Communication and Signal Processing (ICCSP), Chennai, India, 2020.