A novel EN-TLBO-SVR model for analysing achievements of government schemes
by Sabyasachi Mohanty; Sudarsan Padhy; M. Vamsi Krishna
Electronic Government, an International Journal (EG), Vol. 16, No. 3, 2020

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.

Online publication date: Tue, 14-Jul-2020

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