Ensemble of Single‐Layered Complex‐Valued Neural Networks for Classification Tasks

This paper presents ensemble approaches in single-layered complex-valued neural network (CVNN) to solve real-valued classification problems. Each component CVNN of an ensemble uses a recently proposed activation function for its complex-valued neurons (CVNs). A gradient-descent based learning algorithm was used to train the component CVNNs. We applied two ensemble methods, negative correlation learning and bagging, to create the ensembles. Experimental results on a number of real-world benchmark problems showed a substantial performance improvement of the ensembles over the individual single-layered CVNN classifiers. Furthermore, the generalization performances were nearly equivalent to those obtained by the ensembles of real-valued multilayer neural networks

Computer-Aided Diagnosis of Parkinson's Disease Using Complex-Valued Neural Networks and mRMR Feature Selection Algorithm

ABSTRACT Parkinson's disease (PD) is a neurological disorder which has a significant social and economic impact. PD is diagnosed by clinical observation and evaluations, coupled with a PD rating scale. However, these methods may be insufficient, especially in the initial phase of the disease. The processes are tedious and time-consuming, and hence systems that can automatically offer a diagnosis are needed. In this study, a novel method for the diagnosis of PD is proposed. Biomedical sound measurements obtained from continuous phonation samples were used as attributes. First, a minimum redundancy maximum relevance (mRMR) attribute selection algorithm was applied for the identification of the effective attributes. After conversion to a complex number, the resulting attributes are presented as input data to the complex-valued artificial neural network (CVANN). The proposed novel system might be a powerful tool for effective diagnosis of PD

Data Analytics for Credit Risk Models in Retail Banking: a new era for the banking system

Given the nature of the lending industry and its importance for global economic stability, financial institutions have always been keen on estimating the risk profile of their clients. For this reason, in the last few years several sophisticated techniques for modelling credit risk have been developed and implemented. After the financial crisis of 2007-2008, credit risk management has been further expanded and has acquired significant regulatory importance. Specifically, Basel II and III Accords have strengthened the conditions that banks must fulfil to develop their own internal models for estimating the regulatory capital and expected losses. After motivating the importance of credit risk modelling in the banking sector, in this contribution we perform a review of the traditional statistical methods used for credit risk management. Then we focus on more recent techniques based on Machine Learning techniques, and we critically compare tradition and innovation in credit risk modelling. Finally, we present a case study addressing the main steps to practically develop and validate a Probability of Default model for risk prediction via Machine Learning Techniques