Title: A novel EN-TLBO-SVR model for analysing achievements of government schemes

Authors: Sabyasachi Mohanty; Sudarsan Padhy; M. Vamsi Krishna

Addresses: Department of Computer Science and Engineering, Centurion University of Technology and Management, Bhubaneswar, Odisha – 752050, India ' Department of Computer Science and Engineering, Silicon Institute of Technology, Bhubaneswar, Odisha – 751024, India ' Department of Computer Science and Engineering, Chaitanya Institute of Science and Technology, Madhavpatnam, Kakinada, Andhra Pradesh – 533005, India

Abstract: Forecasting physical achievements of welfare and developmental schemes offered by government is always a challenging assignment. Studies undertaken to address this problem using data mining techniques are not fully efficient when the number of features is more than that of samples. This paper presents a novel hybrid machine learning model based on support vector regression, which exhibits magnificent generalisation capability on small samples with proper selection of hyper-parameters using teaching-learning-based optimisation along with elastic net for feature selection. For predicting achievement, a dataset pertaining to housing scheme of Government of India is used where number of samples available is small. It is observed that the proposed hybrid model EN-TLBO-SVR has not only outperformed the use of particle swarm optimisation for hyper-parameter selection, but also kernel principal component analysis and sequential forward floating selection for dimensionality reduction with an additional advantage of identifying significant features present in the samples.

Keywords: support vector regression; SVR; teaching-learning-based optimisation; TLBO; elastic net; predictive analysis; government housing scheme.

DOI: 10.1504/EG.2020.108494

Electronic Government, an International Journal, 2020 Vol.16 No.3, pp.281 - 303

Accepted: 24 Jun 2019
Published online: 16 Jun 2020 *

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