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This paper addresses the problem of extracting fundamental learning style stereotypes through the exploitation of the biologically-inspired pattern recognition paradigm of Artificial Immune Systems (AIS). We present an unsupervised computational mechanism which exhibits the ability to reveal the inherent group structure of learning patterns that pervade a given set of educational profiles. We rely on the construction of an Artificial Immune Network (AIN) of learning style exemplars by proposing a correlation-based distance metric. This choice is actually imposed by the categoric nature of the underlying data. Our work utilizes an original dataset which was derived during the conduction of an extended empirical study involving students of the Hellenic Open University. The educational profiles of the students were built by collecting their answers on a thoroughly designed questionnaire taking into account a wide range of personal characteristics and skills. The efficiency of the proposed approach was assessed in terms of cluster compactness. Specifically, we measured the average correlation deviation of the students' education profiles from the corresponding artificial memory antibodies that represent the acquired learning style stereotypes. Finally, the unsupervised learning procedure adopted in this paper was tested against a correlation-based version of the k-means algorithm indicating a significant improvement in performance for the AIS-based clustering approach. [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]

In this paper, we formulate the recommendation problem as a hybrid combination of one-class classification with collaborative filtering. Specifically, we decompose the recommendation problem into a two-level cascade scheme. In the first level, only desirable items are selected for each user from the large amount of all possible items, taking into account only a small portion of his/her available preferences. This is achieved via a one-class classification scheme trained only with positives examples, i.e. only with desirable items for which users have provided a rating value. In the second level, a collaborative filtering approach is applied to assign a rating degree to the items identified at the first level. The efficiency of our approach is analyzed theoretically in terms of best/worst case scenarios and respective lower/upper mean absolute error (MAE) bounds are computed. Moreover, our approach is experimentally tested against pure collaborative and cascade content-based approaches. The results show that our approach outperforms them in terms of MAE and, moreover, the experimental MAE is close to the theoretical lower bound corresponding to the best case scenario. The superiority of our approach is due to the existence of the one class classifier in the first level of the cascade. [ABSTRACT FROM AUTHOR]