Assessing performance factors in the UK banking sector: A multicriteria methodology

Further consolidation takes place not only among UK banks but also across borders, since some banks see size as a key factor in remaining competitive in international markets. Therefore, it is interesting to investigate the effectiveness and performance of UK banks. Based on their assets, banks are distinguished into small and large ones and a classification of UK banks in a multivariate environment for the period 1998–2002 takes place. The PAIRCLAS multicriteria methodology is employed to investigate the performance of UK small and large banks over multiple criteria, such as asset quality, capital adequacy, liquidity and efficiency/profitability. A comparison with discriminant analysis (DA) and logistic regression (LR) facilitates the investigation of the relative performance of PAIRCLAS against them. The results of the study determine the key factors that specify the classification of a bank as small or large and provide us with the responsible banking decision makers for future readjustments.     

Developing automated valuation models for estimating property values: a comparison of global and locally weighted approaches

Annals of Operations Research - Automated valuation models are widely used in real estate to provide estimates for property prices. Such models are typically developed through regression...     

Operational and economic efficiency analysis of public hospitals in Greece

The continuous growth of hospital costs has driven governments in many countries to seek ways to improve their efficiency. In Greece, this has consistently been a major issue for almost two decades, as efficiency assessment and monitoring systems are lacking. In response to this need, the evaluation of the National Health System hospitals’ efficiency level is a precondition for planning, implementing and monitoring any promising reform. In this paper, a non-parametric modeling approach is employed to assess and analyze the efficiency of 87 Greek public hospitals over the period 2005–2009, using data envelopment analysis. The operational and economic aspects of the hospitals’ operation are considered on the basis of their service/case mix and cost structure. We also investigate the efficiency trends over time with the Malmquist index and a second stage regression analysis is performed to explain the operational and economic efficiency results in terms of the hospitals’ operating characteristics and the environment in which they operate.     

A hybrid ACO-GRASP algorithm for clustering analysis

Cluster analysis is an important tool for data exploration and it has been applied in a wide variety of fields like engineering, economics, computer sciences, life and medical sciences, earth sciences and social sciences. The typical cluster analysis consists of four steps (i.e. feature selection or extraction, clustering algorithm design or selection, cluster validation and results interpretation) with feedback pathway. These steps are closely related to each other and affect the derived clusters. In this paper, a new metaheuristic algorithm is proposed for cluster analysis. This algorithm uses an Ant Colony Optimization to feature selection step and a Greedy Randomized Adaptive Search Procedure to clustering algorithm design step. The proposed algorithm has been applied with very good results to many data sets.     

Inferring robust decision models in multicriteria classification problems: An experimental analysis

Recent research on robust decision aiding has focused on identifying a range of recommendations from preferential information and the selection of representative models compatible with preferential constraints. This study presents an experimental analysis on the relationship between the results of a single decision model (additive value function) and the ones from the full set of compatible models in classification problems. Different optimization formulations for selecting a representative model are tested on artificially generated data sets with varying characteristics.     

An Experimental Evaluation of Some Classification Methods

The classification problem is of major importance to a plethora of research fields. The outgrowth in the development of classification methods has led to the development of several techniques. The objective of this research is to provide some insight on the relative performance of some well-known classification methods, through an experimental analysis covering data sets with different characteristics. The methods used in the analysis include statistical techniques, machine learning methods and multicriteria decision aid. The results of the study can be used to support the design of classification systems and the identification of the proper methods that could be used given the data characteristics.     

Model combination for credit risk assessment: A stacked generalization approach

The development of credit risk assessment models is often considered within a classification context. Recent studies on the development of classification models have shown that a combination of methods often provides improved classification results compared to a single-method approach. Within this context, this study explores the combination of different classification methods in developing efficient models for credit risk assessment. A variety of methods are considered in the combination, including machine learning approaches and statistical techniques. The results illustrate that combined models can outperform individual models for credit risk analysis. The analysis also covers important issues such as the impact of using different parameters for the combined models, the effect of attribute selection, as well as the effects of combining strong or weak models.