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Suche nach „[Fallah Tehrani] [Ali]“ hat 23 Publikationen gefunden
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    Zeitschriftenartikel

    Ali Fallah Tehrani, M. Aggarwal

    Modelling Human Decision Behaviour with Preference Learning

    INFORMS Journal on Computing, vol. 31, no. 2

    2019

    DOI: 10.1287/ijoc.2018.0823

    Abstract anzeigen

    Preferences provide a means for specifying the desires of a decision maker (DM) in a declarative way. In this paper, based on a DM’s pairwise preferences, we infer the DM’s unique decision model. We capture (a) the attitudinal character, (b) relative criteria importance, and (c) the criteria interaction, all of which are specific to the DM. We make use of the preference-learning (PL) technique to induce predictive preference models from empirical data. Because PL is emerging as a new subfield of machine learning, we could use standard machine-learning methods to accomplish our learning objective. We consider the DM’s exemplary preference information in the form of pairwise comparisons between alternatives as the training information. The DM’s decision model is captured in terms of (a), (b), and (c), through the parameters of an attitudinal Choquet integral operator. The proposed learning approach is validated through an experimental study on 16 standard data sets. The superiority of the proposed method in terms of predictive accuracy and easier interpretability is shown both theoretically as well as empirically.

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    Beitrag (Sammelband oder Tagungsband)

    Leon Binder, Ali Fallah Tehrani, P. M. Svasta, Monica I. Ciolacu

    Education 4.0 - Artificial Intelligence Assisted Higher Education: Early Recognition System with Machine Learning to Support Students' Success

    2018 IEEE 24th International Symposium for Design and Technology in Electronic Packaging (SIITME)

    2018

    ISBN: 978-1-5386-5578-8

    DOI: 10.1109/SIITME.2018.8599203

    Abstract anzeigen

    Education 4.0 is being empowered more and more by artificial intelligence (AI) methods. We observe a continuously growing demand for adaptive and personalized Education. In this paper we present an innovative approach to promoting AI in Education 4.0. Our first contribution is AI assisted Higher Education Process with smart sensors and wearable devices for self-regulated learning. Secondly we describe our first results of Education 4.0 didactic methods implemented with learning analytics and machine learning algorithms. The aim of this case study is to predict the final score of students before participating in final examination. We propose an Early Recognition System equipped with real data captured in a blended learning course with a personalized test at the beginning of the semester, an adaptive learning environment based on Auto Tutor by N. A. Crowder theory with adaptive self-assessment feedback. It is obvious that focusing on students' success and their experiences is a win-win scenario for students and professors as well as for the administration.

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    Beitrag (Sammelband oder Tagungsband)

    Ali Fallah Tehrani, Diane Ahrens

    Enhanced Predictive Models for Purchasing in the Fashion Field by Applying Regression Trees Equipped with Ordinal Logistic Regression

    Artificial Intelligence for Fashion Industry in the Big Data Era, Singapore

    2018

    ISBN: 978-981-13-0079-0

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    Zeitschriftenartikel

    Ali Fallah Tehrani, Diane Ahrens

    Modeling Label Dependence for Multi-Label Classification Using the Choquistic Regression

    Pattern Recognition Letters, vol. 92, no. June, pp. 75-80

    2017

    DOI: 10.1016/j.patrec.2017.04.018

    Abstract anzeigen

    While an incorrect identification of underlying dependency in data can lead to a flawed conclusion, recognizing legitimate dependency allows for the opportunity to adapt a model in a correct manner. In this regard, modeling the inter-dependencies in multi-label classification (multi target prediction) is one of the challenging tasks from a machine learning point of view. While common approaches seek to exploit so-called correlated information from labels, this can be improved by assuming the interactions between labels. A well-known tool to model the interaction between attributes is the Choquet integral; it enables one to model non-linear dependencies between attributes. Beyond identifying proper prior knowledge in data (if such knowledge exists), establishing suitable models that are in agreement with prior knowledge is not always a trivial task. In this paper, we propose a first step towards modeling label dependencies for multi-target classifications in terms of positive and negative interactions. In the experimental, we demonstrate real gains by applying this approach.

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    Zeitschriftenartikel

    Ali Fallah Tehrani, Diane Ahrens

    Modified Sequential k‐means Clustering by Utilizing Response: A Case Study for Fashion Products

    Expert Systems, vol. 34, no. 6

    2017

    DOI: 10.1111/exsy.12226

    Abstract anzeigen

    Modified sequential k‐means clustering concerns a k‐means clustering problem in which the clustering machine utilizes output similarity in addition. While conventional clustering methods commonly recognize similar instances at features‐level modified sequential clustering takes advantage of response, too. To this end, the approach we pursue is to enhance the quality of clustering by using some proper information. The information enables the clustering machine to detect more patterns and dependencies that may be relevant. This allows one to determine, for instance, which fashion products exhibit similar behaviour in terms of sales. Unfortunately, conventional clustering methods cannot tackle such cases, because they handle attributes solely at the feature level without considering any response. In this study, we introduce a novel approach underlying minimum conditional entropy clustering and show its advantages in terms of data analytics. In particular, we achieve this by modifying the conventional sequential k‐means algorithm. This modified clustering approach has the ability to reflect the response effect in a consistent manner. To verify the feasibility and the performance of this approach, we conducted several experiments based on real data from the apparel industry.

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    Beitrag (Sammelband oder Tagungsband)

    Heribert Popp, Ali Fallah Tehrani, Monica I. Ciolacu, R. Beer

    Education 4.0 – Fostering Student's Performance with Machine Learning Methods

    Proceedings of the IEEE 23rd International Symposium for Design and Technology in Electronic Packaging (SIITME) [Constanta, Romania; October 26th–29th, 2017]

    2017

    DOI: 10.1109/SIITME.2017.8259941

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    Zeitschriftenartikel

    Ali Fallah Tehrani, Diane Ahrens

    Enhanced predictive models for purchasing in the fashion field by using kernel machine regression equipped with ordinal logistic regression

    Journal of Retailing and Consumer Services, vol. 32, pp. 131-138

    2016

    Abstract anzeigen

    Identifying the products which are highly sold in the fashion apparel industry is one of the challenging tasks, which leads to reduce the write off and increases the revenue. In fact, beyond of sales forecasting in general a crucial question remains whether a product may sell well or not. Assuming three classes as substantial, middle and inconsiderable, the forecasting problem comes down to a classification problem, where the task is to predict the class of a product. In this research, we present a probabilistic approach to identify the class of fashion products in terms of sale. Thereafter, we combine kernel machines with a probabilistic approach to empower the performance of kernel machines and eventually to make use of it to predicting the number of sales. The proposed approach is more robust to outliers (in the case of highly sold products) and in addition uses prior knowledge, hence it serves more reliable results. In order to verify the proposed approach, we conducted several experiments on a real data extracted from an apparel retailer in Germany.

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    Zeitschriftenartikel

    Ali Fallah Tehrani, Diane Ahrens

    Supervised Regression Clustering: A Case Study for Fashion Products

    International Journal of Business Analytics (IJBAN), vol. 3, no. 4, pp. 21-40

    2016

    DOI: 10.4018/IJBAN.2016100102

    Abstract anzeigen

    Clustering techniques typically group similar instances underlying individual attributes by supposing that similar instances have similar attributes characteristic. On contrary, clustering similar instances given a specific behavior is framed through supervised learning. For instance, which fashion products have similar behavior in term of sales. Unfortunately, conventional clustering methods cannot tackle this case, since they handle attributes by a same manner. In fact, conventional clustering approaches do not consider any response, and moreover they assume attributes act by the same importance. However, clustering instances with respect to responses leads to a better data analytics. In this research, the authors introduce an approach for the goal supervised clustering and show its advantage in terms of data analytics as well as prediction. To verify the feasibility and the performance of this approach the authors conducted several experiments on a real dataset derived from an apparel industry.

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    Beitrag (Sammelband oder Tagungsband)

    Ali Fallah Tehrani, Diane Ahrens

    Improved Forecasting and Purchasing of Fashion Products based on the Use of Big Data Techniques

    Supply Management Research, Wiesbaden

    2016

    ISBN: 9783658088088

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    Beitrag (Sammelband oder Tagungsband)

    M. Strickert, Ali Fallah Tehrani, E. Hüllermeier

    The Choquet Kernel for Monotone Data

    Proceedings of the 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN-2014) [April 23rd - 25th 2014, Bruges, Belgium]

    2014

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    Beitrag (Sammelband oder Tagungsband)

    Ali Fallah Tehrani, C. Lebreuche, E. Hüllermeier

    Utilitaristic Choquistic Regression

    Proceedings of the DA2PL'2014 Workshop (From Multiple Criteria Decision Aid to Preference Learning) [November 20-21 2014, Paris, France]

    2014

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    Zeitschriftenartikel

    Ali Fallah Tehrani, E. Hüllermeier, M. Agarwal

    Preference-based Learning of Ideal Solutions in TOPSIS-like Decision Models

    Journal of Multi-Criteria Decision Analysis, vol. 22, no. 3-4, pp. 175-183

    2014

    DOI: 10.1002/mcda.1520

    Abstract anzeigen

    Combining established modelling techniques from multiple-criteria decision aiding with recent algorithmic advances in the emerging field of preference learning, we propose a new method that can be seen as an adaptive version of TOPSIS, the technique for order preference by similarity to ideal solution decision model (or at least a simplified variant of this model). On the basis of exemplary preference information in the form of pairwise comparisons between alternatives, our method seeks to induce an ‘ideal solution’ that, in conjunction with a weight factor for each criterion, represents the preferences of the decision maker. To this end, we resort to probabilistic models of discrete choice and make use of maximum likelihood inference. First experimental results on suitable preference data suggest that our approach is not only intuitively appealing and interesting from an interpretation point of view but also competitive to state-of-the-art preference learning methods in terms of prediction accuracy.

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    Beitrag (Sammelband oder Tagungsband)

    Ali Fallah Tehrani, E. Hüllermeier

    Ordinal Choquistic Regression

    Proceedings of the 8th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2013) [April 23rd - 25th 2013, Milan, Italy]

    2013

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    Beitrag (Sammelband oder Tagungsband)

    Ali Fallah Tehrani, E. Hüllermeier

    Efficient Learning of Classifiers based on the 2-additive Choquet Integral Computational Intelligence

    Computational Intelligence in Intelligent Data Analysis, Berlin; New York, vol. Volume 445

    2013

    ISBN: 978-3-642-32377-5

    Abstract anzeigen

    In a recent work, we proposed a generalization of logistic regression based on the Choquet integral. Our approach, referred to as choquistic regression, makes it possible to capture non-linear dependencies and interactions among predictor variables while preserving two important properties of logistic regression, namely the comprehensibility of the model and the possibility to ensure its monotonicity in individual predictors. Unsurprisingly, these benefits come at the expense of an increased computational complexity of the underlying maximum likelihood estimation. In this paper, we propose two approaches for reducing this complexity in the specific though practically relevant case of the 2-additive Choquet integral. Apart from theoretical results, we also present an experimental study in which we compare the two variants with the original implementation of choquistic regression.

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    Beitrag (Sammelband oder Tagungsband)

    Ali Fallah Tehrani, E. Hüllermeier

    On the VC-Dimension of the Choquet Integral

    Proceedings of the 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2012) [July 9th - 13th 2012, Università degli studi di Catania, Italia]

    2012

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    Zeitschriftenartikel

    Ali Fallah Tehrani, W. Cheng, E. Hüllermeier

    Preference Learning using the Choquet Integral: The Case of Multipartite Ranking

    IEEE Transactions on Fuzzy Systems, vol. 20, no. 6, pp. 1102-1113

    2012

    DOI: 10.1109/TFUZZ.2012.2196050

    Abstract anzeigen

    We propose a novel method for preference learning or, more specifically, learning to rank, where the task is to learn a ranking model that takes a subset of alternatives as input and produces a ranking of these alternatives as output. Just like in the case of conventional classifier learning, training information is provided in the form of a set of labeled instances, with labels or, say, preference degrees taken from an ordered categorical scale. This setting is known as multipartite ranking in the literature. Our approach is based on the idea of using the (discrete) Choquet integral as an underlying model for representing ranking functions. Being an established aggregation function in fields such as multiple criteria decision making and information fusion, the Choquet integral offers a number of interesting properties that make it attractive from a machine learning perspective, too. The learning problem itself comes down to properly specifying the fuzzy measure on which the Choquet integral is defined. This problem is formalized as a margin maximization problem and solved by means of a cutting plane algorithm. The performance of our method is tested on a number of benchmark datasets.

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    Zeitschriftenartikel

    K. Dembczýnski, Ali Fallah Tehrani, W. Cheng, E. Hüllermeier

    Learning Monotone Nonlinear Models using the Choquet Integral

    Machine Learning, vol. 89, no. 1, pp. 183-211

    2012

    DOI: 10.1007/s10994-012-5318-3

    Abstract anzeigen

    The learning of predictive models that guarantee monotonicity in the input variables has received increasing attention in machine learning in recent years. By trend, the difficulty of ensuring monotonicity increases with the flexibility or, say, nonlinearity of a model. In this paper, we advocate the so-called Choquet integral as a tool for learning monotone nonlinear models. While being widely used as a flexible aggregation operator in different fields, such as multiple criteria decision making, the Choquet integral is much less known in machine learning so far. Apart from combining monotonicity and flexibility in a mathematically sound and elegant manner, the Choquet integral has additional features making it attractive from a machine learning point of view. Notably, it offers measures for quantifying the importance of individual predictor variables and the interaction between groups of variables. Analyzing the Choquet integral from a classification perspective, we provide upper and lower bounds on its VC-dimension. Moreover, as a methodological contribution, we propose a generalization of logistic regression. The basic idea of our approach, referred to as choquistic regression, is to replace the linear function of predictor variables, which is commonly used in logistic regression to model the log odds of the positive class, by the Choquet integral. First experimental results are quite promising and suggest that the combination of monotonicity and flexibility offered by the Choquet integral facilitates strong performance in practical applications.

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    Beitrag (Sammelband oder Tagungsband)

    K. Dembczýnski, Ali Fallah Tehrani, W. Cheng, E. Hüllermeier

    Learning Monotone Nonlinear Models using the Choquet Integral

    Machine Learning and Knowledge Discovery in Databases, Berlin [u.a.], vol. 6913 : Lecture Notes in Artificial Intelligence (LNAI)

    2011

    ISBN: 978-3-642-23807-9

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    Vortrag

    Ali Fallah Tehrani

    Echtzeit-Fehlererkennung und Verbesserung der Produktionsqualität in der Glasindustrie durch künstliche Intelligenz

    Posterpräsentation

    6. Tag der Forschung, Deggendorf

    DigitalNachhaltigAngewandte WirtschaftswissenschaftenTC Grafenau

    Vortrag

    Ali Fallah Tehrani

    Echtzeit-Fehlererkennung in der Glasindustrie

    6. Tag der Forschung, Deggendorf