Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Machine Learning Algorithms Based on an Optimization Model

Version 1 : Received: 29 September 2020 / Approved: 30 September 2020 / Online: 30 September 2020 (08:19:51 CEST)

How to cite: Mirmozaffari, M.; Golilarz, N.A.; Band, S.S. Machine Learning Algorithms Based on an Optimization Model. Preprints 2020, 2020090729 (doi: 10.20944/preprints202009.0729.v1). Mirmozaffari, M.; Golilarz, N.A.; Band, S.S. Machine Learning Algorithms Based on an Optimization Model. Preprints 2020, 2020090729 (doi: 10.20944/preprints202009.0729.v1).

Abstract

The main purpose of this paper is to propose a novel optimization model with a new machine learning approach in the first section to achieve the best results in financial institutions in the second section. Since the constancy of efficacy derived from parametric and non-parametric is not significant, this paper provides a scientific assessment at the optimization section and proposes a novel combined parametric and non-parametric model which will be a new experiment in literature perception. A scientific assessment of banks based on a combination of the efficiency measurement method of CCR(Charnes, Cooper and Rhodes model) or CRS(Constant Return to Scale) BCC(Banker, Charnes, and Cooper model) or VRS (Variable Return to Scale) in Data Envelopment Analysis (DEA), as well as Stochastic Frontier Approach (SFA) for 65 banks during Feb to July 2020, are introduced. For analyzing the performance of the parametric and non-parametric approaches we have considered the linear regression and Unreplicated Linear Functional Relationship (ULFR). At the machine learning section, a novel four-layers data mining filtering pre-processes for selected supervised classification as well as unsupervised clustering algorithms to increase the accuracy and to remove unrelated attributes and data are applied. For the four kinds of preprocessing approaches of unsupervised attributes, supervised attributes, supervised instances, and unsupervised instances, we have chosen discretization, attribute selection, stratified remove folds, and resample filters respectively. Based on the nature of the suggested financial institution's dataset and attributes, the most appropriate preprocessing filter in each layer to achieve the highest performance is suggested. Finally, the superior bank, best performance model, and the most accurate algorithm are introduced. The results indicate that the bank number 56 is the superior bank. Among the proposed techniques, the novel recommended CVS compared with CCR-BCC and SFA models, has a more positive correlation with profit risk, and show a higher coefficient of determination values. Sequential Minimal Optimization(SMO) algorithm receives the highest accuracy in all four suggested filtering layers.

Subject Areas

Data Envelopment Analysis; Machine learning; Optimization; Parametric and non-parametric methods; Supervised and unsupervised models; CVS model

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