Browsing by Subject "Decision support systems"
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Item Open Access Adaptive ensemble learning with confidence bounds for personalized diagnosis(AAAI Press, 2016) Tekin, Cem; Yoon, J.; Van Der Schaar, M.With the advances in the field of medical informatics, automated clinical decision support systems are becoming the de facto standard in personalized diagnosis. In order to establish high accuracy and confidence in personalized diagnosis, massive amounts of distributed, heterogeneous, correlated and high-dimensional patient data from different sources such as wearable sensors, mobile applications, Electronic Health Record (EHR) databases etc. need to be processed. This requires learning both locally and globally due to privacy constraints and/or distributed nature of the multimodal medical data. In the last decade, a large number of meta-learning techniques have been proposed in which local learners make online predictions based on their locally-collected data instances, and feed these predictions to an ensemble learner, which fuses them and issues a global prediction. However, most of these works do not provide performance guarantees or, when they do, these guarantees are asymptotic. None of these existing works provide confidence estimates about the issued predictions or rate of learning guarantees for the ensemble learner. In this paper, we provide a systematic ensemble learning method called Hedged Bandits, which comes with both long run (asymptotic) and short run (rate of learning) performance guarantees. Moreover, we show that our proposed method outperforms all existing ensemble learning techniques, even in the presence of concept drift.Item Open Access An analysis of manipulated information and respective alternative costs in information systems and in decision making structures(International Institute of Informatics and Systemics, IIIS, 2006) Güvenen O.; Öztürk, M.H.Today Information Technologies create base for the most important decision support systems for the practices in academia, business and politics. The effectiveness and success of operations that are supported by information systems are directly correlated with the quantity, accuracy, timing, credibility and the quality of the information that prevails in the system. Rapid development of these technologies in recent decades allows high level of information transaction and communication through the whole world. The quantity of information that flows through information systems has increased tremendously. New researches and technological applications in this area aim to improve the systems quantitatively. However, despite a huge and continuous increase in information flow, the quality and reliability of the information in the systems are doubtful from many perspectives. We believe that quality and reliability considerations in information technologies are not handled by researchers and users adequately. So in this research we decided to discuss about quality and reliability aspects of the information flow. To be able to evaluate the information from qualitative perspectives, we believe that it is crucial to handle the problem in science and especially in social sciences by endogenising socio-economic phenomena and science methodology approaches. We hope this work will create a stimulus for researchers of Information Technologies and Systems to give importance to the reliability and quality of information issues.Item Open Access Balance in resource allocation problems: a changing reference approach(2018-05) Erkan, HaleFairness has become one of the primary concerns in resource allocation problems, especially in settings which are associated with public welfare. Using a pure e ciency maximizing approach may not be applicable while distributing resources among entities, hence we propose a novel structure for integrating balance into the allocation process. In the proposed approach, balance is de ned and measured as the deviation from a reference distribution determined by the decision maker. We acknowledge that what is considered balanced by the decision maker might change with respect to the level of total output distributed. To provide an allocation policy that is in line with this changing structure of balance, we allow the decision maker to change her reference distribution depending on the total amount of output (bene t). We illustrate our approach using a project portfolio selection problem. We formulate a mixed integer mathematical programming model for the problem with maximizing e ciency and minimizing imbalance objectives. The bi-objective model is initially solved with the epsilon constraint method. However for larger problem instances this approach fails to nd solutions within reasonable time limits. Hence we implement metaheuristic algorithms and report on their performance. As an alternative solution method, an interactive algorithm is presented and used to nd the most preferred solution of the decision maker. The proposed resource allocation model provides important insights to decision makers regarding the tradeo between e ciency and fairness, and provides a useful tool to incorporate speci c balance concerns into the problem.Item Open Access A comprehensive methodology for determining the most informative mammographic features(2013) Wu, Y.; Alagoz O.; Ayvaci, M.U.S.; Munoz Del Rio, A.; Vanness, D.J.; Woods, R.; Burnside, E.S.This study aims to determine the most informative mammographic features for breast cancer diagnosis using mutual information (MI) analysis. Our Health Insurance Portability and Accountability Act-approved database consists of 44,397 consecutive structured mammography reports for 20,375 patients collected from 2005 to 2008. The reports include demographic risk factors (age, family and personal history of breast cancer, and use of hormone therapy) and mammographic features from the Breast Imaging Reporting and Data System lexicon. We calculated MI using Shannon's entropy measure for each feature with respect to the outcome (benign/malignant using a cancer registry match as reference standard). In order to evaluate the validity of the MI rankings of features, we trained and tested naïve Bayes classifiers on the feature with tenfold cross-validation, and measured the predictive ability using area under the ROC curve (AUC). We used a bootstrapping approach to assess the distributional properties of our estimates, and the DeLong method to compare AUC. Based on MI, we found that mass margins and mass shape were the most informative features for breast cancer diagnosis. Calcification morphology, mass density, and calcification distribution provided predictive information for distinguishing benign and malignant breast findings. Breast composition, associated findings, and special cases provided little information in this task. We also found that the rankings of mammographic features with MI and AUC were generally consistent. MI analysis provides a framework to determine the value of different mammographic features in the pursuit of optimal (i.e., accurate and efficient) breast cancer diagnosis. © 2013 Society for Imaging Informatics in Medicine.Item Open Access Decision support for adopting SPLE with transit-PL(ACM, 2013) Kalender, Mert Emin; Tüzün, E.; Tekinerdoğan, BedirIt is generally acknowledged that the decision to adopt a software product line engineering (SPLE) approach needs to be performed carefully due to the di• erent risks involved in taking such an important decision. To mitigate the potential risks of the transition to SPLE, several studies have been proposed that include many di• erent rules for analyzing the feasibility of the SPLE adoption and the selection of transition process. However, it is not easy to apply these manually and likewise provide a proper decision with the corresponding justification. In this paper, we propose the tool Transit-PL, a web based decision support system for analyzing the feasibility of SPLE for an organization and selecting the appropriate transition strategy. Transit-PL provides a framework to build particular decision support system for selected strategies using di• erent types of questions and corresponding rules and set of answers. Copyright 2013 ACM.Item Open Access The effects of structural characteristics of explanations on use of a DSS(Elsevier, 2006) Gönül, M. S.; Önkal D.; Lawrence, M.Research in the field of expert systems has shown that providing supporting explanations may influence effective use of system developed advice. However, despite many studies showing the less than optimal use made of DSS prepared advice, almost no research has been undertaken to study if the provision of explanations enhances the users' ability to wisely accept DSS advice. This study outlines an experiment to examine the effects of structural characteristics of explanations provided within a forecasting DSS context. In particular, the effects of explanation length (short vs. long) and the conveyed confidence level (weak vs. strong confidence) are examined. Strongly confident and long explanations are found to be more effective in participants' acceptance of interval forecasts. In addition, explanations with higher information value are more effective than those with low information value and thus are persuasive tools in the presentation of advice to users.Item Open Access Estimating the chance of success in IVF treatment using a ranking algorithm(Springer, 2015) Güvenir, H. A.; Misirli, G.; Dilbaz, S.; Ozdegirmenci, O.; Demir, B.; Dilbaz, B.In medicine, estimating the chance of success for treatment is important in deciding whether to begin the treatment or not. This paper focuses on the domain of in vitro fertilization (IVF), where estimating the outcome of a treatment is very crucial in the decision to proceed with treatment for both the clinicians and the infertile couples. IVF treatment is a stressful and costly process. It is very stressful for couples who want to have a baby. If an initial evaluation indicates a low pregnancy rate, decision of the couple may change not to start the IVF treatment. The aim of this study is twofold, firstly, to develop a technique that can be used to estimate the chance of success for a couple who wants to have a baby and secondly, to determine the attributes and their particular values affecting the outcome in IVF treatment. We propose a new technique, called success estimation using a ranking algorithm (SERA), for estimating the success of a treatment using a ranking-based algorithm. The particular ranking algorithm used here is RIMARC. The performance of the new algorithm is compared with two well-known algorithms that assign class probabilities to query instances. The algorithms used in the comparison are Naïve Bayes Classifier and Random Forest. The comparison is done in terms of area under the ROC curve, accuracy and execution time, using tenfold stratified cross-validation. The results indicate that the proposed SERA algorithm has a potential to be used successfully to estimate the probability of success in medical treatment.Item Open Access Fair resource allocation: Using welfare-based dominance constraints(Elsevier, 2022-03-01) Argyris, N.; Karsu, Özlem; Yavuz, MirelIn this paper we consider the problem of supporting resource allocation decisions affecting multiple beneficiaries. Such problems inherently involve efficiency-fairness trade-offs. We introduce a new approach based on the paradigm of maximizing efficiency subject to constraints to ensure that the decision is acceptably fair. In contrast to existing literature, we incorporate fairness in the form of welfare dominance, ensuring that the resultant distribution of benefits to beneficiaries is at least as good as some reference distribution with respect to a set of social welfare functions that satisfy commonly accepted efficiency and fairness related axioms. We introduce a practical means to parameterize the problem, which allows for excluding welfare functions that are deemed insufficiently or overly sensitive to inequality. This allows for analyzing the impact of changes in inequality aversion on efficiency, thus revealing the trade-off between efficiency and fairness. We develop tractable reformulations for the resulting non-linear multi-level optimization problems. We then extend this approach for cases where resources are allocated to groups of individuals with different sizes. We demonstrate the potential use of the suggested framework on two case studies: a workload allocation problem and a healthcare provisioning problem.Item Open Access Feedback-labelling synergies in judgmental stock price forecasting(Elsevier, 2004) Goodwin, P.; Önkal-Atay, D.; Thomson, M. E.; Pollock, A. C.; Macaulay, A.Research has suggested that outcome feedback is less effective than other forms of feedback in promoting learning by users of decision support systems. However, if circumstances can be identified where the effectiveness of outcome feedback can be improved, this offers considerable advantages, given its lower computational demands, ease of understanding and immediacy. An experiment in stock price forecasting was used to compare the effectiveness of outcome and performance feedback: (i) when different forms of probability forecast were required, and (ii) with and without the presence of contextual information provided as labels. For interval forecasts, the effectiveness of outcome feedback came close to that of performance feedback, as long as labels were provided. For directional probability forecasts, outcome feedback was not effective, even if labels were supplied. Implications are discussed and future research directions are suggested.Item Open Access Incorporating just-in-time into a decision support system environment(Elsevier BV, 1991) Oǧuz, Ceyda; Dinçer, CemalIn this paper, a Decision Support System is proposed for a Just-In-Time production system. The Decision Support System includes three components: database, model base, and interface. The database contains the predefined parameters together with the data generated for the considered Just-In-Time production system. In the model base, both deterministic and stochastic aspects of the system are considered. The deterministic system is examined by constructing a linear programming model whereas simulation is used as a tool for the stochastic system. Furthermore, a sensitivity analysis is performed on the Just-In-Time production system with the help of the Decision Support System environment for the unit load size changes under different demand patterns by using the alternative solutions obtained from the model base.Item Open Access Inequity averse optimization in operational research(Elsevier, 2015) Karsu, Ö.; Morton, A.There are many applications across a broad range of business problem domains in which equity is a concern and many well-known operational research (OR) problems such as knapsack, scheduling or assignment problems have been considered from an equity perspective. This shows that equity is both a technically interesting concept and a substantial practical concern. In this paper we review the operational research literature on inequity averse optimization. We focus on the cases where there is a tradeoff between efficiency and equity. We discuss two equity related concerns, namely equitability and balance. Equitability concerns are distinguished from balance concerns depending on whether an underlying anonymity assumption holds. From a modeling point of view, we classify three main approaches to handle equitability concerns: the first approach is based on a Rawlsian principle. The second approach uses an explicit inequality index in the mathematical model. The third approach uses equitable aggregation functions that can represent the DM's preferences, which take into account both efficiency and equity concerns. We also discuss the two main approaches to handle balance: the first approach is based on imbalance indicators, which measure deviation from a reference balanced solution. The second approach is based on scaling the distributions such that balance concerns turn into equitability concerns in the resulting distributions and then one of the approaches to handle equitability concerns can be applied. We briefly describe these approaches and provide a discussion of their advantages and disadvantages. We discuss future research directions focussing on decision support and robustness.Item Open Access İnsan kaynakları yönetiminde kullanılan karar destek sistemlerinin incelenmesi ve değerlendirilmesi(Milli Savunma Üniversitesi, 2011) Karamalak, L.; Özkil, A.; Sabuncuoğlu, İhsanOrganizasyonların rekabetçi ortamlarda yaşamlarını sürdürmeleri için kaynakların etkin kullanılması gerekir. Bu kapsamda, İnsan Kaynakları Yönetimi stratejik bir rol üstlenmektedir. İnsan Kaynakları Yönetiminin stratejik rolünü etkin biçimde oynayabilmesini sağlamak maksadıyla, bilgiyi analiz edebilecek ve çıktılar üretebilecek uygun kalitatif ve kantitatif modeller geliştirilerek karar sürecine katkıda bulunulması gerekir. Bilginin elektronik ortamda toplanmasından ziyade; nasıl, ne zaman, neden ve kimi değerlendirmeli, eğitmeli, ödüllendirmeli, seçmeli vb. sorulara cevap verecek Karar Destek Sitemleri geliştirilmelidir. Bu çalışmada; İnsan Kaynakları Yönetimi işlevleri hakkında özet bilgi verilmekte ve literatürdeki çalışmalar taranarak, Karar Destek Sistemlerinin söz konusu işlevlerde ne şekilde kullanıldığı araştırılmaktadır. Ayrıca, İnsan Kaynakları Yönetimine yönelik Karar Destek Sistemlerinde kullanılan yöntemlerin işlevler bazında sınıflandırılması sunulmaktadır.Item Open Access Joint lot sizing and tool management in a CNC environment(Elsevier, 1999) Aktuük, M. S.; Önen, S.We propose a new algorithm to solve lot sizing, tool allocation and machining conditions optimization problems simultaneously to minimize total production cost in a CNC environment. Most of the existing lot sizing and tool management methods solve these problems independently using a two-level optimization approach. Thus, we not only improve the overall solution by exploiting the interactions among these decision making problems, but also prevent any infeasibility that might occur for the tool management problem due to decisions made at the lot sizing level. The computational experiments showed that in a set of randomly generated problems 22.5% of solutions found by the two-level approach were infeasible and the proposed joint approach improved the solution on the average by 6.79% for the remaining cases.Item Open Access Judgmental adjustments of previously adjusted forecasts(Wiley-Blackwell Publishing, 2008) Önkal D.; Gönül, M. S.; Lawrence, M.Forecasts are important components of information systems. They provide a means for knowledge sharing and thus have significant decision-making impact. In many organizations, it is quite common for forecast users to receive predictions that have previously been adjusted by providers or other users of forecasts. Current work investigates some of the factors that may influence the size and propensity of further adjustments on already-adjusted forecasts. Two studies are reported that focus on the potential effects of adjustment framing (Study 1) and the availability of explanations and/or original forecasts alongside the adjusted forecasts (Study 2). Study 1 provides evidence that the interval forecasts that are labeled as "adjusted" are modified less than the so-called "original/unadjusted" predictions. Study 2 suggests that the provision of original forecasts and the presence of explanations accompanying the adjusted forecasts serve as significant factors shaping the size and propensity of further modifications. Findings of both studies highlight the importance of forecasting format and user perceptions with critical organizational repercussions.Item Open Access Order of limits in reputations(Springer, 2016) Dalkıran, N. A.The fact that small departures from complete information might have large effects on the set of equilibrium payoffs draws interest in the adverse selection approach to study reputations in repeated games. It is well known that these large effects on the set of equilibrium payoffs rely on long-run players being arbitrarily patient. We study reputation games where a long-run player plays a fixed stage-game against an infinite sequence of short-run players under imperfect public monitoring. We show that in such games, introducing arbitrarily small incomplete information does not open the possibility of new equilibrium payoffs far from the complete information equilibrium payoff set. This holds true no matter how patient the long-run player is, as long as her discount factor is fixed. This result highlights the fact that the aforementioned large effects arise due to an order of limits argument, as anticipated. © 2016, Springer Science+Business Media New York.Item Open Access Predicting pilot behavior in medium-scale scenarios using game theory and reinforcement learning(American Institute of Aeronautics and Astronautics Inc., 2014) Yildiz, Y.; Agogino, A.; Brat, G.A key element to meet the continuing growth in air traffic is the increased use of automation. Decision support systems, computer-based information acquisition, trajectory planning systems, high-level graphic display systems, and all advisory systems are considered to be automation components related to next generation (NextGen) air space. Given a set of goals represented as reward functions, the actions of the players may be predicted. However, several challenges need to be overcome. First, determining how a player can attempt to maximize their reward function can be a difficult inverse problem. Second, players may not be able to perfectly maximize their reward functions. ADS-B technology can provide pilots the information, position, velocity, etc. of other aircraft. However, a pilot has limited ability to use all this information for his/her decision making. For this scenario, the authors model these pilot limitations by assuming that pilots can observe a limited section of the grid in front of them.Item Open Access Ranking instances by maximizing the area under ROC curve(Institute of Electrical and Electronics Engineers, 2013) Guvenir, H. A.; Kurtcephe, M.In recent years, the problem of learning a real-valued function that induces a ranking over an instance space has gained importance in machine learning literature. Here, we propose a supervised algorithm that learns a ranking function, called ranking instances by maximizing the area under the ROC curve (RIMARC). Since the area under the ROC curve (AUC) is a widely accepted performance measure for evaluating the quality of ranking, the algorithm aims to maximize the AUC value directly. For a single categorical feature, we show the necessary and sufficient condition that any ranking function must satisfy to achieve the maximum AUC. We also sketch a method to discretize a continuous feature in a way to reach the maximum AUC as well. RIMARC uses a heuristic to extend this maximization to all features of a data set. The ranking function learned by the RIMARC algorithm is in a human-readable form; therefore, it provides valuable information to domain experts for decision making. Performance of RIMARC is evaluated on many real-life data sets by using different state-of-the-art algorithms. Evaluations of the AUC metric show that RIMARC achieves significantly better performance compared to other similar methods. © 1989-2012 IEEE.Item Open Access A simulation-based support tool for data-driven decision making: operational testing for dependence modeling(IEEE, 2014) Biller, B.; Akçay, Alp; Çorlu, C.; Tayur, S.Dependencies occur naturally between input processes of many manufacturing and service applications. When the dependence parameters are known with certainty, the failure to factor the dependencies into decisions is well known to waste significant resources in system management. Our focus is on the case of unknown dependence parameters that must be estimated from finite amounts of historical input data. In this case, the estimates of the unknown dependence parameters are random variables and simulations are designed to account for the dependence parameter uncertainty to better support the data-driven decision making. The premise of our paper is that there are certain cases in which the assumption of an independent input process to minimize the expected cost of input parameter uncertainty becomes preferable to accounting for the dependence parameter uncertainty in the simulation. Therefore, a fundamental question to answer before capturing the dependence parameter uncertainty in a stochastic system simulation is whether there is sufficient statistical evidence to represent the dependence, despite the uncertainty around its estimate, in the presence of limited data. We seek an answer for this question within a data-driven inventory-management context by considering an intermittent demand process with correlated demand size and number of interdemand periods. We propose two new finite-sample hypothesis tests to serve as the decision support tools determining when to ignore the correlation and when to account for the correlation together with the uncertainty around its estimate. We show that a statistical test accounting for the expected cost of correlation parameter uncertainty tends to reject the independence assumption less frequently than a statistical test which only considers the sampling distribution of the correlation-parameter estimator. The use of these tests is illustrated with examples and insights are provided into operational testing for dependence modeling.