The effects of customer segmentation, borrower behaviors and analytical methods on the performance of credit scoring models in the agribusiness sector

The main aim of this study is to analyse the joint effects of customer segmentation, borrowers\u27 characteristics and modelling techniques on the classification accuracy of a scoring model for agribusinesses. To this end, we used data provided by a Chilean company on 161,163 loans from January 2007 to December 2013. We considered random forest, neural network and logistic regression models as analytical methods. Regarding the borrowers\u27 profiles, we examined the effects of socio-demographic, repayment-behaviour, agribusiness-specific and credit-related variables. We also segmented the customers as individuals, SMEs and large holdings. As the segments show different risk behaviours, we obtained a better performance when we estimated a scoring model for each segment instead of using a segmentation variable. In terms of the value of each set of variables, behavioural variables increased the predictive capability of the model by double the amount achieved by including agribusiness-related variables. The random forest is the model with the best classification accuracy

Intelligent Biosignal Analysis Methods

This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others

A Probabilistic Assessment of Failure for Air Force Building Systems

Deteriorating and failing federal facilities represent a cost to leaders and organizations as they attempt to manage and maintain these assets. Currently the Air Force employs the BUILDERTM Sustainment Management System to predict the reliability of building components. At different system levels, however, the probabilities of failure are not predicted. The purpose of this research is to provide probabilistic models which predict the probability of failure at the system level of a building s infrastructure hierarchy. This research investigated the plumbing, HVAC, fire protection, and electrical systems. Probabilistic models were created for these systems by using fault trees with fuzzy logic on the basis of risk by weighting the probabilities of failure by the consequences of failure. This thesis then validated each of the models using real-world Air Force work order data. Through contingency analysis, it was found that the current BUILDERTM condition index model possessed no predictive ability due to the resulting p-value of 1.00; the probabilistic models possessed much more predictive ability with a resulting p-value of 0.12. The probabilistic models are statistically shown to be a significant improvement over the current condition index model; these models lead to improved decision making for infrastructure assets

A Perceptual Comparison of “Black Box” Modeling Algorithms for Nonlinear Audio Systems

Nonlinear systems identification is a widespread topic of interest, particularly within the audio industry, as these techniques are employed to synthesize black box models of nonlinear audio effects. Given the myriad approaches to black box modeling, questions arise as to whether an “optimal” approach exists, or one that achieves valid subjective results as a model with minimal computational expense. This thesis uses ABX listening tests to compare black box models of three hardware audio effects using two popular nonlinear implementations, along with two proposed modified implementations. Models were constructed in the Hammerstein form using sine sweeps and a novel measurement technique for the filters and nonlinearities, respectively. Testing revolved around null hypotheses assuming no change in model identification regardless of the device modeled, implementation used, or program material of the model stimulus. Results provide clear evidence of an effect on all of these accounts, and support a full rejection of the null hypotheses. Outcomes demonstrate a preferable implementation out of the algorithms tested, and suggest the removal of certain implementations as valid approaches altogether

Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field