International Journal of Data Analysis Techniques and Strategies
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International Journal of Data Analysis Techniques and Strategies (2 papers in press)
An efficient Mahalanobis-Taguchi System (e-MTS) for Nonlinear Multiclass Classification Problem by Vinay Kumar, Sasadhar Bera, Indrajit Mukherjee, Anuradha Sarkar Abstract: Mahalanobis-Taguchi System (MTS) is a robust predictive analytics technique used for diagnostic, forecasting, nonlinear classification, and feature selection of multivariate systems. MTS concept is applied in fault detection, medical diagnostic, and health hazards. However, most of the work on MTS addressed binary or two-class classification problems. There seems to be a lack of research that illustrates suitability and compares MTS performance for various nonlinear multiclass classification problems. This work's primary objective is to propose an efficient MTS (e-MTS) for nonlinear multiclass classification problems. A new approach to defining threshold Mahalanobis distance (MD) is also suggested to improve the classification performance of MTS. The secondary aim of this research is to illustrate the efficiency of e-MTS and compare its performance with back-propagation artificial neural network (BPNN) and Radial Basis Function (RBF) kernel-based support vector machine (SVM). Analysis of various cases confirms the suitability of e-MTS for multiclass classification. Keywords: Multiclass classification; Mahalanobis-Taguchi system; Support Vector Machine; Artificial Neural Network.
Application of Machine Learning in Banking and Finance: A Bibliometric Analysis by Rahul Dubey, Arti Chandani Abstract: Machine Learning (ML) is a type of Artificial Intelligence (AI) that empowers a framework to gain from information instead of through express programming. It uses different models to identify and provide solution, which are based on data, to the problems being faced by the industry. The application of machine learning techniques is not limited to the one sector and it has been used in healthcare, fraud detection, automation among others. ML empowers models to train on information collections prior to being deployed. Machine learning algorithms are used in finance to identify fraud, automate trading, underwrite loans, and give financial advice to investors. The bibliometric analytical approach has not yet been applied to the construct application of Machine Learning (ML) in banking and finance. The study attempts to connect machine learning into banking and finance which is intrinsically a multidisciplinary study. We have, in our paper tried to identify and put across a comprehensive analysis of the publications made in this construct so as to make significant contributions on the existing body of knowledge in this field with facts that are statistically backed using bibliometric analysis. This study will provide insights to faculty, researchers and industry experts about the research trends and the possible avenues for future research in this construct. Keywords: Machine Learning; Artificial Intelligence; Banking; Finance; Bibliometric Analysis; Citation Analysis.