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Suche nach „[E.] [Hüllermeier]“ hat 9 Publikationen gefunden
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    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

    TC Grafenau

    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.

    TC Grafenau

    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

    TC Grafenau

    Beitrag (Sammelband oder Tagungsband)

    E. Hüllermeier, Ali Fallah Tehrani

    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.

    TC Grafenau

    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.

    TC Grafenau

    Zeitschriftenartikel

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

    TC Grafenau

    Beitrag (Sammelband oder Tagungsband)

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

    TC Grafenau

    Beitrag (Sammelband oder Tagungsband)

    E. Hüllermeier, Ali Fallah Tehrani

    On the VC-Dimension of the Choquet Integral

    Advances in Computational Intelligence, Part I: 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2012, Università degli studi di Catania, Catania, Italy, July 9-13, 2012., vol. 297

    ISBN: 9783642317088