Abstract: This paper addresses the problem of recommendation with the context of one-class classification. Specifically, we employ the alternative machine learning framework of Artificial Immune Systems (AIS) in order to develop user-specific preference models. Our approach is based on the fact that users experience a major difficulty in articulating their music preferences while at the same time they are extremely reluctant in providing counter-examples of their music habits. Therefore, developing coherent user models on the grounds of both positive (desirable) and negative (non-desirable) training samples is not a feasible task since the class of non-favorable data patterns is severely under-represented. Our…recommendation approach alleviates the need to collect negative feedback form the user by building recommendation models that exclusively rely on the presence of a limited number positive data items. Such models are built, however, by trying to efficiently cover the complementary space of non-desirable patterns. This is achieved through the utilization of V-Detector, an AIS-based one-class classification algorithm, which operates by developing a set of variable-sized detectors for the subspace of non-preferable music items. V-Detector, despite being exclusively fed with instances from the positive class, focuses on delivering an accurate model of the negative space. Based on this complementarity, our recommendation algorithm is able to implicitly model individual user preferences that span arbitrary-shaped and fragmented regions of the complete space of patterns. The proposed recommendation approach was experimentally evaluated in terms of its efficiency to correctly identify the user-defined classes of positive and negative preference. The obtained results justify its superiority against traditional one-class classification approaches.
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Keywords: Artificial immune systems, recommender systems, one class classification, negative selection SVM data description
Abstract: The doctrine of the “one size fits all” approach in the field of disease diagnosis and patient management is being replaced by a more per patient approach known as “personalized medicine”. In this spirit, biomarkers are key variables in the research and development of new methods for prognostic and classification model training based on advances in the field of artificial intelligence [1 , 2 , 3 ]. Metabolomics refers to the systematic study of the unique chemical fingerprints that cellular processes leave behind. The metabolic profile of a person can provide a snapshot of cell physiology and, by extension, metabolomics provide…a direct “functional reading of the physiological state” of an organism. Via employing machine learning methodologies, a general evaluation chart of nutritional biomarkers is formulated and an optimised prediction method for body to mass index is investigated with the aim to discover dietary patterns.
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