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Suche nach „[Fallah Tehrani] [Ali]“ hat 25 Publikationen gefunden
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    DigitalAngewandte InformatikTC Grafenau

    Zeitschriftenartikel

    Ali Fallah Tehrani, M. Strickert, Diane Ahrens

    Class of Monotone Kernelized Classifiers on the basis of the Choquet Integral

    Expert Systems, vol. 37, no. First published: 21 January 2020, pp. 1-15

    2020

    DOI: 10.1111/exsy.12506

    Abstract anzeigen

    The key property of monotone classifiers is that increasing (decreasing) input values lead to increasing (decreasing) the output value. Preserving monotonicity for a classifier typically requires many constraints to be respected by modeling approaches such as artificial intelligence techniques. The type of constraints strongly depends on the modeling assumptions. Of course, for sophisticated models such conditions might be very complex. In this study we present a new family of kernels that we call it Choquet kernels. Henceforth it allows for employing popular kernel‐based methods such as support vector machines. Instead of a naïve approach with exponential computational complexity we propose an equivalent formulation with quadratic time in the number of attributes. Furthermore, since coefficients derived from kernel solutions are not necessarily monotone in the dual form, different approaches are proposed to monotonize coefficients. Finally experiments illustrate beneficial properties of the Choquet kernels.

    DigitalAngewandte InformatikTC Grafenau

    Zeitschriftenartikel

    Ali Fallah Tehrani, M. Strickert, Diane Ahrens

    A Class of Monotone Kernelized Classifiers on the Basis of the Chopuet Integral

    [Accepted for publication]

    Expert Systems

    2020

    DigitalNachhaltigAngewandte InformatikTC Grafenau

    Vortrag

    Ali Fallah Tehrani

    Fehlererkennung in Echtzeit für Glasindustrie mit ML

    KI-Arbeitskreis "Maschinelles Lernen", Deggendorf

    2019

    DigitalNachhaltigAngewandte InformatikAngewandte WirtschaftswissenschaftenTC Grafenau

    Vortrag

    Ali Fallah Tehrani

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

    Posterpräsentation

    6. Tag der Forschung, Deggendorf

    2019

    DigitalAngewandte WirtschaftswissenschaftenTC Grafenau

    Zeitschriftenartikel

    M. Aggarwal, Ali Fallah Tehrani

    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.

    DigitalAngewandte InformatikTC Grafenau

    Beitrag (Sammelband oder Tagungsband)

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

    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.

    DigitalAngewandte InformatikTC Grafenau

    Vortrag

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

    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), Iasi, Romania

    2018

    DigitalAngewandte WirtschaftswissenschaftenTC Grafenau

    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

    DigitalTC Grafenau

    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.

    DigitalNachhaltigTC Grafenau

    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.

    DigitalAngewandte InformatikTC Grafenau

    Beitrag (Sammelband oder Tagungsband)

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

    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

    DigitalTC Grafenau

    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.

    DigitalTC Grafenau

    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.

    Angewandte InformatikTC Grafenau

    Vortrag

    Ali Fallah Tehrani

    Was Frauen wollen - Absatzprognosen im Modehandel durch künstliche Intelligenz

    3. Tag der Forschung - Themenbereiche Wirtschaft und Gesundheit, Deggendorf

    2016

    DigitalNachhaltigTC Grafenau

    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

    Angewandte InformatikTC Grafenau

    Vortrag

    Ali Fallah Tehrani

    Learning Classifiers on the Use of Monotone Learning

    1. Bayerisch-Tschechische Wissenschaftskonferenz "Datenanalyse", Jindřichův Hradec, Tschechische Republik

    2015

    TC Grafenau

    Beitrag (Sammelband oder Tagungsband)

    Ali Fallah Tehrani, M. Strickert, 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

    TC Grafenau

    Zeitschriftenartikel

    M. Agarwal, Ali Fallah Tehrani, E. Hüllermeier

    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