Dionisios N. Sotiropoulos

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We explore the use of objective audio signal features to model the individualized (subjective) perception of similarity between music files. We present MUSIPER, a content-based music retrieval system which constructs music similarity perception models of its users by associating different music similarity measures to different users. Specifically, a(More)
We address the problem of modeling the subjective perception of similarity between two music files that have been extracted from a music database with use of objective features. We propose the importation of user models in Content-Based Music Retrieval systems, which embody the ability of evolving and using different music similarity measures for different(More)
In this paper, we compare the performance of Artificial Immune System (AIS)-based classification algorithms to the performance of Gaussian kernel-based Support Vector Machines (SVM) in problems with a high degree of class imbalance. Our experimentation indicates that the AIS-based classification paradigm has the intrinsic properly of dealing more(More)
This is paper addresses the problem of tracking the time evolution of communities within co-authorship networks. We consider an evolutionary clustering approach which adapts the statistical framework of shrinkage estimation to obtain a smoothed version of the overall affinity matrix as the optimal weighted average between the matrices of past and current(More)
Kinetics of hydrolysis of aqueous dispersions of arsono-, sulfo-, phosphono- and phospholipids by phospholipase A2 from pig pancreas are characterized in terms of interfacial rate and equilibrium parameters. The enzyme with or without calcium binds with high affinity to the aqueous dispersions of the four classes of anionic lipids and shows the same general(More)
We investigate the effect of the Class Imbalance Problem on the performance of an Artificial Immune System(AIS)-based classification algorithm. Our motivation stems from the fact that the Adaptive Immune System constitutes one of the most sophisticated biological systems which is particularly evolved in order to continuously address an extremely unbalanced(More)
In this paper, we develop and evaluate a hybrid classification method based on a combination of Artificial Immune Systems and Genetic Algorithms. Specifically, the new algorithm, called Genetic-AIRS, is a combination of the Artificial Immune Resource System (AIRS) algorithm with well-known evolutionary computation techniques. As a result, Genetic-AIRS is(More)