Impute, Select, Decision Tree and Naïve Bayes (ISE-DNC): An Ensemble Learning Approach to Classify the Lung Cancer

9 Pages Posted: 19 Oct 2020

See all articles by Bhanumathi S

Bhanumathi S

S J C Institute of Technology

Dr. Chandrashekara S N

C Byregowda Institute of Technology

Date Written: August 5, 2020

Abstract

In this work, we have introduced a hybrid novel approach to classify the lung cancer data using ensemble learning. According to this approach, first of all, we present data preprocessing model where missing values are imputed with the help of knn. Later, we incorporated filtering-based feature selection to reduce the feature dimension. Later, decision tree and Naïve Bayes classifiers are used to create the ensemble learner. Finally, voting based decisions are made to classify the data. The proposed approach is represented as ISE-DNC (Impute, Select, Decision Tree and Naïve Bayes) classifier. The proposed approach is implemented on two lung cancer public datasets which are obtained from the UCI repository. The experimental study shows that the proposed approach achieves 96.87% and 89.78% of classification accuracy for lung cancer and thoracic surgery dataset.

Suggested Citation

S, Bhanumathi and S N, Dr. Chandrashekara, Impute, Select, Decision Tree and Naïve Bayes (ISE-DNC): An Ensemble Learning Approach to Classify the Lung Cancer (August 5, 2020). Proceedings of the Second International Conference on Emerging Trends in Science & Technologies For Engineering Systems (ICETSE-2019), Available at SSRN: https://ssrn.com/abstract=3667438 or http://dx.doi.org/10.2139/ssrn.3667438

Bhanumathi S (Contact Author)

S J C Institute of Technology ( email )

B.B. Road
Chikkaballapur, 562 101
India

Dr. Chandrashekara S N

C Byregowda Institute of Technology ( email )

kolar
India

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