Conference Paper

Analyzing Sensory Data Using Non-linear Preference Learning with Feature Subset Selection

DOI: 10.1007/978-3-540-30115-8_28 Conference: Machine Learning: ECML 2004, 15th European Conference on Machine Learning, Pisa, Italy, September 20-24, 2004, Proceedings
Source: DBLP


The quality of food can be assessed from dierent points of view. In this paper, we deal with those aspects that can be appreciated through sensory impressions. When we are aiming to induce a function that maps object descriptions into ratings, we must consider that con- sumers' ratings are just a way to express their preferences about the products presented in the same testing session. Therefore, we postu- late to learn from consumers' preference judgments instead of using an approach based on regression. This requires the use of special purpose kernels and feature subset selection methods. We illustrate the benefits of our approach in two families of real-world data bases.


Available from: Gustavo Fernandez Bayon
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    • "Unfortunately , this approach leads us to deal with datasets of size n 2 when the original size of S is only n. This mean that some applications become intractable, although other times this approach was successfully used [5] [6] [7] [8] [3] [9]. To alleviate the difficulties caused by the size of datasets, the main problem is that (as happens with the AUC) Herbrich's loss function can not be expressed as a sum of disagreements or errors produced by each input x i ∈ X. "
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    ABSTRACT: Learning tasks where the set Y of classes has an ordering relation arise in a number of important application fields. In this context, the loss function may be defined in different ways, ranging from multiclass classification to ordinal or metric regression. However, to consider only the ordered structure of Y, a measure of goodness of a hypothesis h has to be related to the number of pairs whose relative ordering is swapped by h. In this paper, we present a method, based on the use of a multivariate version of Support Vector Machines (SVM) that learns to order minimizing the number of swapped pairs. Finally, using benchmark datasets, we compare the scores so achieved with those found by other alternative approaches.
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    • "Then the learner will return a classifier built on a reduced set of wavelengths. This strategy to explain a classification procedure has been successfully employed in a number of application fields [4] [20]. "
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    ABSTRACT: The classification of animal feed ingredients has become a challenging computational task since the food crisis that arose in the European Union after the outbreak of bovine spongiform encephalopathy (BSE). The most interesting alternative to replace visual observation under classical microscopy is based on the use of near infrared reflectance microscopy (NIRM). This technique collects spectral information from a set of microscopic particles of animal feeds. These spectra can be classified using maximum margin classifiers with good results. However, it is difficult to interpret the models in terms of the contribution of features. To gain insight into the interpretability of such classifications, we propose a method that learns accurate classifiers defined on a small set of narrow intervals of wavelengths. The proposed method is a greedy bipartite procedure that may be successfully compared with other state-of-the-art feature selectors and can be scaled up efficiently to deal with other classification tasks of higher dimensionality.
    Information Sciences 08/2013; 241:58–69. DOI:10.1016/j.ins.2013.03.054 · 4.04 Impact Factor
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    • "This can be explained as follows. As pointed out in [13], only trained experts, e.g., wine tasters, can maintain a consistent mapping throughout a given session, and untrained users' mappings generally change for each response. It is known that users' responses are roughly correlated, but can drift slightly [5]. "
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    ABSTRACT: A recommender system has to collect users' preference data. To collect such data, rating or scoring methods that use rating scales, such as good-fair-poor or a five-point-scale, have been employed. We replaced such collection methods with a ranking method, in which objects are sorted according to the degree of a user's preference. We developed a technique to convert the rankings to scores based on order statistics theory. This technique successfully improved the accuracy of ranking recommended items. However, we targeted only memory-based recommendation algorithms. To test whether or not the use of ranking methods and our conversion technique are effective for wide variety of recommenders, we apply our conversion technique to model-based algorithms.
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