Topological applications of multilayer perceptrons and support vector machines in financial decision support systems

2018 ◽  
Vol 24 (1) ◽  
pp. 474-507 ◽  
Author(s):  
Mohammad Zoynul Abedin ◽  
Chi Guotai ◽  
Fahmida-E- Moula ◽  
A.S.M. Sohel Azad ◽  
Mohammed Shamim Uddin Khan
Keyword(s):  
Decision Support ◽  
Support Vector Machines ◽  
Decision Support Systems ◽  
Support Systems ◽  
Support Vector ◽  
Multilayer Perceptrons ◽  
Vector Machines ◽  
Financial Decision

Designing personalized intelligent financial decision support systems

1999 ◽  
Vol 26 (1) ◽  
pp. 31-47 ◽  
Author(s):  
António Palma-dos-Reis ◽  
Fatemeh “Mariam” Zahedi
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Financial Decision

Decision Support with Support Vector Machinesin R & D Project Termination Decision

2011 ◽  
Vol 403-408 ◽  
pp. 4098-4102
Author(s):  
Jing Rong Dong ◽  
Yu Ke Chen
Keyword(s):  
Reinforcement Learning ◽  
Decision Support ◽  
Project Management ◽  
Statistical Analysis ◽  
Support Vector Machines ◽  
Evaluation Criteria ◽  
Support Vector ◽  
Selection Decisions ◽  
Vector Machines ◽  
Termination Decisions

Research and development (R&D) project termination decision is an important and challenging task for organizations with R&D project management .Current research on R&D project management mainly focuses on project selection decisions. Very little research has been done on the termination decision of R&D projects .In this paper a support vector machines classifer for assisting managers in deciding whether to abandon an ongoing R&D project at various stages of R&D is presented. It has also shown by the modeling and pattern recognizing results in terms of termination decisions of fifty R&D projects that the method possesses reinforcement learning properties and universalized capabilities. With respect to modeling and termination decision of R&D project, which has the fact that the evaluation criteria are hardly ever determined by conventional approaches such as statistical analysis, the method is available.

scholarly journals An Equivalence Between Sparse Approximation and Support Vector Machines

1998 ◽  
Vol 10 (6) ◽  
pp. 1455-1480 ◽  
Author(s):  
Federico Girosi
Keyword(s):  
Support Vector Machines ◽  
Sparse Approximation ◽  
Basis Functions ◽  
Approximation Schemes ◽  
Support Vector ◽  
Multilayer Perceptrons ◽  
Basis Pursuit ◽  
Large Set ◽  
Regularization Theory ◽  
Vector Machines

This article shows a relationship between two different approximation techniques: the support vector machines (SVM), proposed by V. Vapnik (1995) and a sparse approximation scheme that resembles the basis pursuit denoising algorithm (Chen, 1995; Chen, Donoho, & Saunders, 1995). SVM is a technique that can be derived from the structural risk minimization principle (Vapnik, 1982) and can be used to estimate the parameters of several different approximation schemes, including radial basis functions, algebraic and trigonometric polynomials, B-splines, and some forms of multilayer perceptrons. Basis pursuit denoising is a sparse approximation technique in which a function is reconstructed by using a small number of basis functions chosen from a large set (the dictionary). We show that if the data are noiseless, the modified version of basis pursuit denoising proposed in this article is equivalent to SVM in the following sense: if applied to the same data set, the two techniques give the same solution, which is obtained by solving the same quadratic programming problem. In the appendix, we present a derivation of the SVM technique in the framework of regularization theory, rather than statistical learning theory, establishing a connection between SVM, sparse approximation, and regularization theory.

Automatic feature weighting for improving financial Decision Support Systems

2018 ◽  
Vol 107 ◽  
pp. 78-87 ◽  
Author(s):  
Yosimar Oswaldo Serrano-Silva ◽  
Yenny Villuendas-Rey ◽  
Cornelio Yáñez-Márquez
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Feature Weighting ◽  
Financial Decision

scholarly journals Forecasting Dengue, Chikungunya and Zika cases in Recife, Brazil: a spatio-temporal approach based on climate conditions, health notifications and machine learning

2021 ◽  
Vol 10 (12) ◽  
pp. e452101220804
Author(s):  
Cecilia Cordeiro da Silva ◽  
Clarisse Lins de Lima ◽  
Ana Clara Gomes da Silva ◽  
Giselle Machado Magalhães Moreno ◽  
Anwar Musah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Aedes Aegypti ◽  
Temporal Analysis ◽  
Support Vector ◽  
Multilayer Perceptrons ◽  
Monitoring And Control ◽  
Linear Regression Models ◽  
Vector Machines ◽  
Spatio Temporal

Dengue has become a challenge for many countries. Arboviruses transmitted by Aedes aegypti spread rapidly over the last decades. The emergence chikungunya fever and zika in South America poses new challenges to vector monitoring and control. This situation got worse from 2015 and 2016, with the rapid spread of chikungunya, causing fever and muscle weakness, and Zika virus, related to cases of microcephaly in newborns and the occurrence of Guillain-Barret syndrome, an autoimmune disease that affects the nervous system. The objective of this work was to construct a tool to forecast the distribution of arboviruses transmitted by the mosquito Aedes aegypti by implementing dengue, zika and chikungunya transmission predictors based on machine learning, focused on multilayer perceptrons neural networks, support vector machines and linear regression models. As a case study, we investigated forecasting models to predict the spatio-temporal distribution of cases from primary health notification data and climate variables (wind velocity, temperature and pluviometry) from Recife, Brazil, from 2013 to 2016, including 2015’s outbreak. The use of spatio-temporal analysis over multilayer perceptrons and support vector machines results proved to be very effective in predicting the distribution of arbovirus cases. The models indicate that the southern and western regions of Recife were very susceptible to outbreaks in the period under investigation. The proposed approach could be useful to support health managers and epidemiologists to prevent outbreaks of arboviruses transmitted by Aedes aegypti and promote public policies for health promotion and sanitation.

scholarly journals Decision Support System (DSS) for Fraud Detection in Health Insurance Claims Using Genetic Support Vector Machines (GSVMs)

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Robert A. Sowah ◽  
Marcellinus Kuuboore ◽  
Abdul Ofoli ◽  
Samuel Kwofie ◽  
Louis Asiedu ◽  
...  
Keyword(s):  
Health Insurance ◽  
Decision Support ◽  
Support Vector Machines ◽  
Service Providers ◽  
National Health Insurance Scheme ◽  
Support Vector ◽  
Insurance Claims ◽  
Insurance Scheme ◽  
Vector Machines ◽  
Health Insurance Claims

Fraud in health insurance claims has become a significant problem whose rampant growth has deeply affected the global delivery of health services. In addition to financial losses incurred, patients who genuinely need medical care suffer because service providers are not paid on time as a result of delays in the manual vetting of their claims and are therefore unwilling to continue offering their services. Health insurance claims fraud is committed through service providers, insurance subscribers, and insurance companies. The need for the development of a decision support system (DSS) for accurate, automated claim processing to offset the attendant challenges faced by the National Health Insurance Scheme cannot be overstated. This paper utilized the National Health Insurance Scheme claims dataset obtained from hospitals in Ghana for detecting health insurance fraud and other anomalies. Genetic support vector machines (GSVMs), a novel hybridized data mining and statistical machine learning tool, which provide a set of sophisticated algorithms for the automatic detection of fraudulent claims in these health insurance databases are used. The experimental results have proven that the GSVM possessed better detection and classification performance when applied using SVM kernel classifiers. Three GSVM classifiers were evaluated and their results compared. Experimental results show a significant reduction in computational time on claims processing while increasing classification accuracy via the various SVM classifiers (linear (80.67%), polynomial (81.22%), and radial basis function (RBF) kernel (87.91%).

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