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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. [ABSTRACT FROM AUTHOR]

This paper focuses on a special category of machine learning problems arising in cases where the set of available training instances is significantly biased towards a particular class of patterns. Our work addresses the so-called Class Imbalance Problem through the utilization of an Artificial Immune System-(AIS)based classification algorithm which encodes the inherent ability of the Adaptive Immune System to mediate the exceptionally imbalanced 'self' / 'non-self' discrimination process. From a computational point of view, this process constitutes an extremely imbalanced pattern classification task since the vast majority of molecular patterns pertain to the 'non-self' space. Our work focuses on investigating the effect of the class imbalance problem on the AIS-based classification algorithm by assessing its relative ability to deal with extremely skewed datasets when compared against two state-of-the-art machine learning paradigms such as Support Vector Machines (SVMs) and Multi-Layer Perceptrons (MLPs). To this end, we conducted a series of experiments on a music-related dataset where a small fraction of positive samples was to be recognized against the vast volume of negative samples. The results obtained indicate that the utilized bio-inspired classifier outperforms SVMs in detecting patterns from the minority class while its performance on the same task is competently close to the one exhibited by MLPs. Our findings suggest that the AIS-based classifier relies on its intrinsic resampling and class-balancing functionality in order to address the class imbalance problem. [ABSTRACT FROM AUTHOR]