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11.
Disaggregation methods have become popular in multicriteria decision aiding (MCDA) for eliciting preferential information and constructing decision models from decision examples. From a statistical point of view, data mining and machine learning are also involved with similar problems, mainly with regard to identifying patterns and extracting knowledge from data. Recent research has also focused on the introduction of specific domain knowledge in machine learning algorithms. Thus, the connections between disaggregation methods in MCDA and traditional machine learning tools are becoming stronger. In this paper the relationships between the two fields are explored. The differences and similarities between the two approaches are identified, and a review is given regarding the integration of the two fields. 相似文献
12.
Chrysovalantis Gaganis Fotios Pasiouras Michael Doumpos Constantin Zopounidis 《Optimization Letters》2010,4(4):543-558
Banking crises can be damaging for the economy, and as the recent experience has shown, nowadays they can spread rapidly across
the globe with contagious effects. Therefore, the assessment of the stability of a county’s banking sector is important for
regulators, depositors, investors and the general public. In the present study, we propose the development of classification
models that assign the banking sectors of various countries in three classes, labelled “low stability”, “medium stability”,
and “high stability”. The models are developed using three multicriteria decision aid techniques, which are well-suited to
ordinal classification problems. We use a sample of 114 banking sectors (i.e., countries), and a set of criteria that includes
indicators of the macroeconomic, institutional and regulatory environment, as well as basic characteristics of the banking
and financial sector. The models are developed and tested using a tenfold cross-validation approach and they are benchmarked
against models developed with discriminant analysis and logistic regression. 相似文献
13.
Constantin Zopounidis Michael Doumpos 《The Journal of the Operational Research Society》1999,50(11):1138-1148
Business failure prediction is one of the most essential problems in the field of financial management. The research on developing quantitative business failure prediction models has been focused on building discriminant models to distinguish among failed and non-failed firms. Several researchers in this field have proposed multivariate statistical discrimination techniques. This paper explores the applicability of multicriteria analysis to predict business failure. Four preference disaggregation methods, namely the UTADIS method and three of its variants, are compared to three well-known multivariate statistical and econometric techniques, namely discriminant analysis, logit and probit analyses. A basic (learning) sample and a holdout (testing) sample are used to perform the comparison. Through this comparison, the relative performance of all the aforementioned methods is investigated regarding their discriminating and predicting ability. 相似文献
14.
15.
A comparison of several nearest neighbor classifier metrics using Tabu Search algorithm for the feature selection problem 总被引:1,自引:0,他引:1
Magdalene Marinaki Yannis Marinakis Michael Doumpos Nikolaos Matsatsinis Constantin Zopounidis 《Optimization Letters》2008,2(3):299-308
The feature selection problem is an interesting and important topic which is relevant for a variety of database applications.
This paper utilizes the Tabu Search metaheuristic algorithm to implement a feature subset selection procedure while the nearest
neighbor classification method is used for the classification task. Tabu Search is a general metaheuristic procedure that
is used in order to guide the search to obtain good solutions in complex solution spaces. Several metrics are used in the
nearest neighbor classification method, such as the euclidean distance, the Standardized Euclidean distance, the Mahalanobis
distance, the City block metric, the Cosine distance and the Correlation distance, in order to identify the most significant
metric for the nearest neighbor classifier. The performance of the proposed algorithms is tested using various benchmark datasets
from UCI Machine Learning Repository. 相似文献
16.
K. Kosmidou F. Pasiouras M. Doumpos C. Zopounidis 《Computational Management Science》2004,1(3-4):329-343
Although the banking sector in the UK is one of the most open and it is characterized by an increasing foreign bank presence, it remains relatively under-researched compared to studies for other countries. The objective of this paper is to investigate the performance of the UK banking sector focusing on the performance of the domestic banks as opposed to the performance of the foreign banks in order to test the hypothesis of higher performance of the domestic banks in a developed market. For this purpose, the UTADIS multicriteria methodology is employed to compare domestic and foreign banks performance over multiple criteria, such as profitability, liquidity, risk and efficiency, using a data sample covering 26 domestic and 32 foreign banks operating in the UK over the period 1998 to 2001. The results of the study, support the home advantage hypothesis, suggesting that the higher performance of domestic banks compared to foreign banks is also the case in the UK. The most important distinguishing performance factors between the two groups of banks are interest revenue to total earning assets, and profit before taxes to loans plus securities, which are higher for the domestic banks.AMS classification:
91B28 相似文献
17.
Disaggregation methods have been extensively used in multiple criteria decision making to infer preferential information from
reference examples, using linear programming techniques. This paper proposes simple extensions of existing formulations, based
on the concept of regularization which has been introduced within the context of the statistical learning theory. The properties
of the resulting new formulations are analyzed for both ranking and classification problems and experimental results are presented
demonstrating the improved performance of the proposed formulations over the ones traditionally used in preference disaggregation
analysis. 相似文献
18.
Optimization of nearest neighbor classifiers via metaheuristic algorithms for credit risk assessment
Yannis Marinakis Magdalene Marinaki Michael Doumpos Nikolaos Matsatsinis Constantin Zopounidis 《Journal of Global Optimization》2008,42(2):279-293
The classification problem consists of using some known objects, usually described by a large vector of features, to induce
a model that classifies others into known classes. The present paper deals with the optimization of Nearest Neighbor Classifiers
via Metaheuristic Algorithms. The Metaheuristic Algorithms used include tabu search, genetic algorithms and ant colony optimization.
The performance of the proposed algorithms is tested using data from 1411 firms derived from the loan portfolio of a leading
Greek Commercial Bank in order to classify the firms in different groups representing different levels of credit risk. Also,
a comparison of the algorithm with other methods such as UTADIS, SVM, CART, and other classification methods is performed
using these data. 相似文献
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20.
Vassilios BabalosNikolaos Philippas Michael Doumpos Constantin Zopounidis 《Applied mathematics and computation》2012,218(9):5693-5703
Mutual fund investors are concerned with the selection of the best fund in terms of performance among the set of alternative funds. This paper proposes an innovative mutual funds performance evaluation measure in the context of multicriteria decision making. We implement a multicriteria methodology using stochastic multicriteria acceptability analysis, on Greek domestic equity funds for the period 2000-2009. Combining a unique dataset of risk-adjusted returns such as Carhart’s alpha with funds’ cost variables, we obtain a multicriteria performance evaluation and ranking of the mutual funds, by means of an additive value function model. The main conclusion is that among employed variables, the sophisticated Carhart’s alpha plays the most important role in determining fund rankings. On the other hand, funds’ rankings are affected only marginally by operational attributes. We believe that our results could have serious implications either in terms of a fund rating system or for constructing optimal combinations of portfolios. 相似文献