Rough set methodology in meta-analysis - a comparative and exploratory analysis

We study the applicability of the pattern recognition methodology "rough set data analysis" (RSDA) in the field of meta analysis. We give a summary of the mathematical and statistical background and then proceed to an application of the theory to a meta analysis of empirical studies dealing with the deterrent effect introduced by Becker and Ehrlich. Results are compared with a previously devised meta regression analysis. We find that the RSDA can be used to discover information overlooked by other methods, to preprocess the data for further studying and to strengthen results previously found by other methods.Rough Data Set, RSDA, Meta Analysis, Data Mining, Pattern Recognition, Deterrence, Criminometrics

Machine learning and statistical techniques : an application to the prediction of insolvency in Spanish non-life insurance companies

Prediction of insurance companies insolvency has arisen as an important problem in the field of financial research. Most methods applied in the past to tackle this issue are traditional statistical techniques which use financial ratios as explicative variables. However, these variables often do not satisfy statistical assumptions, which complicates the application of the mentioned methods. In this paper, a comparative study of the performance of two non-parametric machine learning techniques (See5 and Rough Set) is carried out. We have applied the two methods to the problem of the prediction of insolvency of Spanish non-life insurance companies, upon the basis of a set of financial ratios. We also compare these methods with three classical and well-known techniques: one of them belonging to the field of Machine Learning (Multilayer Perceptron) and two statistical ones (Linear Discriminant Analysis and Logistic Regression). Results indicate a higher performance of the machine learning techniques. Furthermore, See5 and Rough Set provide easily understandable and interpretable decision models, which shows that these methods can be a useful tool to evaluate insolvency of insurance firms.El pronóstico sobre la insolvencia de las compañías de seguro ha surgido como un problema importante en el ámbito de investigación financiera. La mayoría de los métodos aplicados en el pasado para abordar este problema, son técnicas estadísticas tradicionales que usan los ratios financieros como variables explicativas. Aunque, estas variables a menudo no satisfacen las suposiciones estadísticas, lo que complica la aplicación de dichos métodos. En este artículo, se lleva a cabo un estudio comparativo sobre la actuación de dos técnicas de aprendizaje automático no paramétrico (See5 y Rough Set). Hemos aplicado ambos métodos al problema del pronóstico sobre la insolvencia de compañías españolas de seguros no de vida, sobre la base de un conjunto de ratios financieros. Además, hemos comparado estos métodos con tres técnicas clásicas y muy conocidas: una de ellas perteneciente al área del Aprendizaje Automático (Perceptrón Multicapa), y dos estadísticos (Análisis Discriminante Lineal y Regresión Logística). Los resultados indican un desempeño más elevado en las técnicas de aprendizaje automático. Es más, See5 y Rough Set aportan unos modelos de decisión fácilmente entendibles, e interpretables, lo que demuestra que estos métodos pueden ser útiles para evaluar la insolvencia de empresas de seguros

Uncertainty Management of Intelligent Feature Selection in Wireless Sensor Networks

Wireless sensor networks (WSN) are envisioned to revolutionize the paradigm of monitoring complex real-world systems at a very high resolution. However, the deployment of a large number of unattended sensor nodes in hostile environments, frequent changes of environment dynamics, and severe resource constraints pose uncertainties and limit the potential use of WSN in complex real-world applications. Although uncertainty management in Artificial Intelligence (AI) is well developed and well investigated, its implications in wireless sensor environments are inadequately addressed. This dissertation addresses uncertainty management issues of spatio-temporal patterns generated from sensor data. It provides a framework for characterizing spatio-temporal pattern in WSN. Using rough set theory and temporal reasoning a novel formalism has been developed to characterize and quantify the uncertainties in predicting spatio-temporal patterns from sensor data. This research also uncovers the trade-off among the uncertainty measures, which can be used to develop a multi-objective optimization model for real-time decision making in sensor data aggregation and samplin

An Improvement on Extended Kalman Filter for Neural Network Training

Information overload has resulted in difficulties of managing and processing information. Reduction of data using well-defined techniques such as rough set may provide a means to overcome this problem. Extracting useful imformation and knowledge from data is a major concern in information science. Artificial intelligence systems, such as neural network systems, are widely used to extract and infer knowledge from databases. This study explored the training of a neural network inference system using the extended Kalman filter (EKF) learning algorithm. The inference accuracy, inference duration and training performance of this extended Kalman filter neural network were compared with the standard back-propagation algorithm and an improved version of the back-propagation neural network learning algorithm. It was discovered that the extended Kalman filter trained neural network required les