@conference {34, title = {Conic Multi-task Classification}, booktitle = {Machine Learning and Knowledge Discovery in Databases - ECML/PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part II}, year = {2014}, note = {

Acceptance rate 23.8\% (115/483).

}, publisher = {Springer}, organization = {Springer}, address = {Nancy, France}, abstract = {

Traditionally, Multi-task Learning (MTL) models optimize the average of task-related objective functions, which is an intuitive approach and which we will be referring to as Average MTL. However, a more general framework, referred to as Conic MTL, can be formulated by considering conic combinations of the objective functions instead; in this framework, Average MTL arises as a special case, when all combination coefficients equal 1. Although the advantage of Conic MTL over Average MTL has been shown experimentally in previous works, no theoretical justification has been provided to date. In this paper, we derive a generalization bound for the Conic MTL method, and demonstrate that the tightest bound is not necessarily achieved, when all combination coefficients equal 1; hence, Average MTL may not always be the optimal choice, and it is important to consider Conic MTL. As a byproduct of the generalization bound, it also theoretically explains the good experimental results of previous relevant works. Finally, we propose a new Conic MTL model, whose conic combination coefficients minimize the generalization bound, instead of choosing them heuristically as has been done in previous methods. The rationale and advantage of our model is demonstrated and verified via a series of experiments by comparing with several other methods.

}, isbn = {978-3-662-44850-2}, doi = {10.1007/978-3-662-44851-9_13}, url = {http://dx.doi.org/10.1007/978-3-662-44851-9_13}, author = {Cong Li and Michael Georgiopoulos and Georgios C. Anagnostopoulos}, editor = {Toon Calders and Floriana Esposito and Eyke H{\"u}llermeier and Rosa Meo} } @article {44, title = {An Adaptive Multiobjective Approach to Evolving ART Architectures}, journal = {Neural Networks, IEEE Transactions on}, volume = {21}, year = {2010}, month = {April}, pages = {529-550}, abstract = {

In this paper, we present the evolution of adaptive resonance theory (ART) neural network architectures (classifiers) using a multiobjective optimization approach. In particular, we propose the use of a multiobjective evolutionary approach to simultaneously evolve the weights and the topology of three well-known ART architectures; fuzzy ARTMAP (FAM), ellipsoidal ARTMAP (EAM), and Gaussian ARTMAP (GAM). We refer to the resulting architectures as MO-GFAM, MO-GEAM, and MO-GGAM, and collectively as MO-GART. The major advantage of MO-GART is that it produces a number of solutions for the classification problem at hand that have different levels of merit [accuracy on unseen data (generalization) and size (number of categories created)]. MO-GART is shown to be more elegant (does not require user intervention to define the network parameters), more effective (of better accuracy and smaller size), and more efficient (faster to produce the solution networks) than other ART neural network architectures that have appeared in the literature. Furthermore, MO-GART is shown to be competitive with other popular classifiers, such as classification and regression tree (CART) and support vector machines (SVMs).

}, keywords = {adaptive multiobjective approach, adaptive resonance theory, Algorithms, ART architecture, ART neural nets, ART neural network architecture, Artificial Intelligence, ARTMAP, Automated, category proliferation, classification, classification problem, Computer Simulation, ellipsoidal ARTMAP, evolutionary computation, fuzzy ARTMAP, Fuzzy Logic, fuzzy neural nets, Gaussian ARTMAP, genetic algorithms (GAs), genetic operators, Humans, Machine learning, multiobjective evolutionary approach, multiobjective optimization, neural net architecture, Neural Networks (Computer), Normal Distribution, optimisation, pattern recognition}, issn = {1045-9227}, doi = {10.1109/TNN.2009.2037813}, author = {Assem Kaylani and Michael Georgiopoulos and Mansooreh Mollaghasemi and Georgios C. Anagnostopoulos and Christopher Sentelle and Mingyu Zhong} } @conference {42, title = {Metric representations of data via the Kernel-based Sammon Mapping}, booktitle = {Neural Networks (IJCNN), The 2010 International Joint Conference on}, year = {2010}, note = {

Acceptance rate 71.5\% (621/868).

}, month = {July}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, organization = {Institute of Electrical and Electronics Engineers (IEEE)}, address = {Barcelona, Spain}, abstract = {

In this paper we present a novel generalization of Sammon{\textquoteright}s Mapping (SM), which is a popular, metric multi-dimensional scaling technique used in data analysis and visualization. The new approach, namely the Kernel-based Sammon Mapping (KSM), yields the classic SM and other much related techniques as special cases. Apart from being able to approximate distance-preserving projections, it can also learn to metrically represent arbitrarily-defined dissimilarities or similarities between samples. Moreover, it can handle equally well numeric, categorical or mixed-type data. It is able to accomplish all this by modeling its projections as linear combinations of appropriate kernel functions. We report experimental results, which showcase KSM{\textquoteright}s capabilities in visually representing several meaningful relationships between samples of selected datasets.

}, keywords = {data analysis, data visualisation, data visualization, distance-preserving projections, kernel-based Sammon mapping, metric multidimensional scaling technique, metric representations, Visualization}, doi = {10.1109/IJCNN.2010.5596662}, author = {Mingbo Ma and Ryan Gonet and RuiZhi Yu and Georgios C. Anagnostopoulos} } @conference {43, title = {Multi-objective memetic evolution of ART-based classifiers}, booktitle = {Evolutionary Computation (CEC), 2010 IEEE Congress on}, year = {2010}, note = {

Nominated for Best Paper Award.

Acceptance rate 60\% (622/1027).

}, month = {July}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, organization = {Institute of Electrical and Electronics Engineers (IEEE)}, address = {Barcelona, Spain}, abstract = {

In this paper we present a novel framework for evolving ART-based classification models, which we refer to as MOME-ART. The new training framework aims to evolve populations of ART classifiers to optimize both their classification error and their structural complexity. Towards this end, it combines the use of interacting sub-populations, some traditional elements of genetic algorithms to evolve these populations and a simulated annealing process used for solution refinement to eventually give rise to a multi-objective, memetic evolutionary framework. In order to demonstrate its capabilities, we utilize the new framework to train populations of semi-supervised Fuzzy ARTMAP and compare them with similar networks trained via the recently published MO-GART framework, which has been shown as being very effective in yielding high-quality ART-based classifiers. The experimental results show clear advantages of MOME-ART in terms of Pareto Front quality and density, as well as parsimony properties of the resulting classifiers.

}, keywords = {adaptive resonance theory, ART classifiers, ART-based classification models, ART-based classifiers, classification error, Complexity theory, Cooling, Electronic mail, fuzzy set theory, genetic algorithms, interacting subpopulations, Mathematical model, MO-GART framework, MOME-ART, multiobjective memetic evolutionary framework, Pareto front quality, Pareto optimisation, parsimony property, pattern classification, semisupervised fuzzy ARTMAP, simulated annealing, simulated annealing process, solution refinement, structural complexity, Subspace constraints, Training, training framework}, doi = {10.1109/CEC.2010.5586385}, author = {Rong Li and Timothy R. Mersch and Oriana X. Wen and Assem Kaylani and Georgios C. Anagnostopoulos} }