Hasan Bal - Academia.edu (original) (raw)

Papers by Hasan Bal

Aksaray üniversitesi iktisadi ve idari bilimler fakültesi dergisi, Aug 31, 2017

Bu çalışmada çok girdili ve çok çıktılı süreçlerde karar verme birimlerinin göreli etkinliklerini... more Bu çalışmada çok girdili ve çok çıktılı süreçlerde karar verme birimlerinin göreli etkinliklerinin ölçümünde kullanılan, parametrik olmayan yöntemlerden Veri Zarflama Analizi (VZA) ve zaman serisi analizi ile performans sıralaması sağlayan Multimora yöntemleri incelenmiştir. Bu yöntemlerle, 2008-2013 dönemlerinde Türk bankacılık sektöründe faaliyet gösteren 25 bankanın etkinlik değerleri hesaplanmış ve hangi bankaların etkin olup hangilerinin etkin olmadığı tespit edilmiştir. Elde edilen sonuçlar multimora metodunun etkinlik ölçümlerinde kullanılabileceğini göstermektedir.

gazi university journal of science, Jun 19, 2017

This paper deals with joint use Canonical correlation analysis (CCA) and Data envelopment analysi... more This paper deals with joint use Canonical correlation analysis (CCA) and Data envelopment analysis (DEA) techniques. CCA is a multivariate statistical technique that can be used to determine the relationship between two multiple variable sets. DEA is a nonparametric approach for measuring the relative efficiency of peer decision making units(DMUs) when multiple inputs and outputs are present. Data envelopment analysis (DEA) model selection is problematic. The estimated efficiency for any DMU depends on the inputs and outputs included in the model. A benefical method for model selection is proposed in this paper. Efficiencies are calculated for all possible DEA model specifications. The results are analysed using Canonical Correlation Analysis. It is shown that model equivalence or dissimilarity can be easily assessed using this approach. The aim of this study is to get an effective result by using the CCA for the correct model choice in DEA. For this purpose, data set of airports in Turkey were used. The correlation calculations are carried out to understand the nature of the relationship between the models of DEA. It is aimed to find the most effective DEA model by using CCA technique.

INTERNATIONAL CONFERENCE ON ADVANCES IN NATURAL AND APPLIED SCIENCES: ICANAS 2016, 2016

Data envelopment analysis (DEA) is a linear programming (LP) technique for measuring the relative... more Data envelopment analysis (DEA) is a linear programming (LP) technique for measuring the relative efficiency of peer decision making units(DMUs) when multiple inputs and outputs are present. This objective method was originated by Charnes et al. (1978). DEA can be used, not only for estimating the performance of units, but also for solving other problems of management such as aggregating several preference rankings into single ranking. Data Envelopment Analysis (DEA) model selection is an important step and problematic. Efficiency values for decision making units are connected to input and output data. It also depends on the number of outputs plus inputs. A new method for model selection is proposed in this study. Efficiencies are calculated for all possible DEA model specifications. It is shown that model equivalence or dissimilarity can be easily assessed using this approach. The results are analysed using Principal Component Analysis.

Computers & Operations Research, 2011

In this paper we introduce a goal programming formulation for the multi-group classification prob... more In this paper we introduce a goal programming formulation for the multi-group classification problem. Although a great number of mathematical programming models for two-group classification problems have been proposed in the literature, there are few mathematical programming models for multi-group classification problems. Newly proposed multi-group mathematical programming model is compared with other conventional multi-group methods by using different real data sets taken from the literature and simulation data. A comparative analysis on the real data sets and simulation data shows that our goal programming formulation may suggest efficient alternative to traditional statistical methods and mathematical programming formulations for the multi-group classification problem.

Expert Systems With Applications, Jul 1, 2019

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service... more This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights  This paper proposes a neutral cross efficiency model for basic two-stage systems  Proposed model can obtain more realistic weight scheme than basic two stage systems  Proposed model can fully rank the units in overall (system) and sub-stages  The system efficiency is the product of the sub-stages efficiencies

Expert Systems With Applications, Apr 1, 2011

... of the minimum deviation approaches, goal programming approaches, mixed-integer programming a... more ... of the minimum deviation approaches, goal programming approaches, mixed-integer programming approaches and ... The results based on real-data and simulation show that classification ... for information processing based on a connectionist approach (Wongseree, Chaiyaratana ...

Iranian Journal of Science and Technology Transaction A-science, Mar 24, 2017

The Burr III distribution is a very popular distribution for modeling real data in terms of risk,... more The Burr III distribution is a very popular distribution for modeling real data in terms of risk, reliability, and process capability, and thus the estimation of its parameters is essential in most real applications. The classical estimation methods, such as maximum likelihood and least squares, are often used to estimate the parameters of the Burr III distribution. However, maximizing the likelihood function developed for the parameter estimation of the four-parameter Burr type III distribution is a quite difficult problem. Hence, the heuristic approaches must be used to discover good solutions. Particle swarm optimization (PSO) is one of the heuristic approaches, which is a population-based technique developed from swarm intelligence. This paper proposes an alternative parameter estimation method for Burr III distribution using the PSO heuristic approach. Simulation results show that the PSO approach provides accurate estimates and the PSO method is satisfactory for the parameter estimation of Burr III distribution.

Computers & Operations Research, 2010

Politeknik dergisi, Nov 27, 2022

❖ Doğrusal olmayan sınıflandırma problemleri için yeni bir yaklaşım / A new approach for nonlinea... more ❖ Doğrusal olmayan sınıflandırma problemleri için yeni bir yaklaşım / A new approach for nonlinear classification problems ❖ Destek vektör regresyon analizi ile sınıflandırma skorlarının elde edilişi / Obtaining classification scores by support vector regression analysis ❖ Matematiksel programlama ile sınıflandırma kuralı belirlenmesi / Determination of classification rule with mathematical programming ❖ Farklı öznitelikler ile epileptik nöbet tespiti / Detection of epileptic seizures with different features

Dumlupınar Üniversitesi sosyal bilimler dergisi, Nov 6, 2016

Veri Zarflama Analizi, karar verme birimlerinin göreli etkinliklerinin ölçen doğrusal programlama... more Veri Zarflama Analizi, karar verme birimlerinin göreli etkinliklerinin ölçen doğrusal programlamaya dayalı bir parametrik olmayan yöntemdir. Bu yöntem çeşitli girdilerle bazı çıktıları üreten üniversiteler, hastaneler ve bankalar gibi homojen karar verme birimlerinin etkinliklerinin değerlendirilmesi sıklıkla kullanılmaktadır. Etkinlik skorlarının hesaplanmasında seçilen girdi ve çıktıların oldukça büyük öneme sahiptir. Bu yüzden doğru girdi ve çıktıları seçmek için literatürde veri zarflama analizinin çok farklı modelleri bulunmaktadır. Bu çalışmada 28 şehre ait veriler kullanılarak ekonomik performanslar hesaplanmıştır. Olası bütün veri zarflama analizi modelleri etkinlik skorları hesaplanarak sonuçlar Temel Bileşen Analizi kullanılarak analiz edilmiştir.

gazi university journal of science, Jan 14, 2011

In spite of the abundance of articles on mathematical programming models to the two-group classif... more In spite of the abundance of articles on mathematical programming models to the two-group classification problem, very few have addressed the multi-group classification problem using mathematical programming. This study presents a new multi-group data classification method based on mathematical programming. A new multi-group data classification model is proposed in this study that includes the strong properties of the mathematical programming models previously suggested for multi-group classification problems in the literature. The efficiency of proposed approach is tested on the well-known IRIS data set. The results on the IRIS data set show that our proposed method is usability and efficient on multi-group classification problems.

Mathematical and computational applications, Apr 1, 2014

This study aims to determine the technical efficiency levels of the enterprises active in the "Ma... more This study aims to determine the technical efficiency levels of the enterprises active in the "Manufacture of Basic Iron and Steel and of Ferro-Alloys" sector in Turkey. The inputs and outputs are deterministic in classical Data Envelopment Analysis, so the changes in exchange rate, inflation rate, etc. aren't considered, and the precautions for future inconsistencies are not foreseen. This leads to critics of deterministic Data Envelopment Analysis models. In this paper, the additive model developed depending on the Banker, Charnes and Cooper (BCC) model was extended by chance constrained programming formulations in order to overcome the insufficiencies in deterministic Data Envelopment Analysis, and the technical analysis of "Manufacture of Basic Iron and Steel and of Ferro-Alloys" sector was performed.

DergiPark (Istanbul University), Mar 24, 2010

In this study, new classification models were developed which can be used in the solution to the ... more In this study, new classification models were developed which can be used in the solution to the problems of Discriminant Analysis having two groups. For the solution of these type of problems, Lam, Choo and Moy (1996) proposed a model regarding the minimization of deviations from the group means. The model examined by these authors loses its efficiency in respect of the hit ratio as the distributions of populations of samples considered go away from the normal distribution. For the samples drawn from non normal or skewed distributions, the median is a much more suitable descriptive statistic than the mean. The aim of the study is to consider the models of two-group classification problems by minimizing the deviations from the group medians. When these proposed approaches are applied to the data of real life or of simulation drawn from different distributions, it is observed that the attained performance of classification is better than both some important classification approaches in the literature and especially the classification performance minimizing the deviations from group means proposed by Lam, Choo and Moy.

Hacettepe Journal of Mathematics and Statistics, Feb 1, 2007

Discriminant Analysis is a method for determining group classifications for a set of similar unit... more Discriminant Analysis is a method for determining group classifications for a set of similar units or observations. A number of new efficient mathematical programming approaches have been developed as an alternative to examining classification problems using statistical models. In this study two new mathematical programming approaches are developed for the minimization of the sum of the deviations and the concept of relative efficiency for Data Envelopment Analysis when solving the two group classification problem. The efficiency and practicability of the suggested approaches are supported with a simulation study involving three different distributions and different cases for the units in the groups.

DergiPark (Istanbul University), Jan 24, 2012

The cross-efficiency evaluation (CEE) method, which was developed as a contribution to classical ... more The cross-efficiency evaluation (CEE) method, which was developed as a contribution to classical Data Envelopment Analysis (DEA), has been successively used to solve problems involving unrealistic weight distribution as well as problems that do not require prior information for ranking the decision making units (DMUs). Originally, the CEE method included the efficiency evaluations that were obtained for a DMU by the classical DEA for the reuse of optimal weights in the other DMUs. As the optimal weights in the classical DEA solutions usually have multiple solutions, this reduces the usefulness of the CEE method. Hence, this study suggests a new technique that could be used in the second stage of the CEE method for removing the problem of multiple optimal weights and for determining the reasonable ranks of DMUs. The performance of the proposed model is examined on real data set relative to the efficiencies of Turkey cities.

DergiPark (Istanbul University), Aug 13, 2010

In this paper, a new classification model which combines the discriminant analysis and the data e... more In this paper, a new classification model which combines the discriminant analysis and the data envelopment analysis and bases on on the multicriteria decision making is developed. Our suggested model utilizes the relative efficiency concept of the data envelopment analysis in predicting group membership of units. The study is supported with an application which examines a few selected socioeconomic indicators of some member and candidate countries of European Union.

Annals of Operations Research, Jun 19, 2015

Having multiple optimal solutions to weights affects to a great extent the consistency of operati... more Having multiple optimal solutions to weights affects to a great extent the consistency of operations related to weights. The cross efficiency method is the most frequently studied topic in data envelopment analysis (DEA) literature. Originally, the cross efficiency method included the efficiency evaluations that were obtained for a decision making unit (DMU) by the classical DEA for the reuse of optimal weights in other DMUs. As the optimal weights in classical DEA solutions usually have multiple solutions, this reduces the usefulness of the cross evaluation. Lam (J Oper Res Soc 61:134–143, 2010) proposed a mixed-integer linear programming (MILP) formulation based on linear discriminant analysis and super efficiency method to choose suitable weight sets to be used in cross efficiency evaluation. In this study, Lam’s MILP model has been modified to reduce the steps during the solution process. The model also becomes a linear programming model after the modification to make it easier to use and to reduce the computational complexity. Numerical examples indicate that the proposed weight determination model both reduces the steps and minimizes computational complexity. Furthermore, it has similar performance with Lam’s MILP model for the cross efficiency evaluation.

Mathematical and computational applications, Apr 1, 2002

gazi university journal of science, 2007

In this study, a multi-criteria data envelopment analysis (MCDEA) model, used in the literature t... more In this study, a multi-criteria data envelopment analysis (MCDEA) model, used in the literature to moderate the homogeneity of weights dispersion, is solved using pre-emptive goal programming. The MCDEA model solved using pre-emptive goal programming gives the same relative efficiency as the classical DEA model while it improves the homogeneity of input-output weights. This conclusion is confirmed by the computational results obtained when the two models are applied to a real data set relative to the socioeconomic performances of European countries and to randomly generated instances with various numbers of decision making units, inputs and outputs.