@conference {57, title = {Hash Function Learning via Codewords}, booktitle = {Machine Learning and Knowledge Discovery in Databases - ECML/PKDD 2015}, series = {Lecture Notes in Computer Science}, volume = {9284}, year = {2015}, note = {Acceptance rate 23.4\% (89/380).}, pages = {659-674}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {In this paper we introduce a novel hash learning framework that has two main distinguishing features, when compared to past approaches. First, it utilizes codewords in the Hamming space as ancillary means to accomplish its hash learning task. These codewords, which are inferred from the data, attempt to capture similarity aspects of the data{\textquoteright}s hash codes. Secondly and more importantly, the same framework is capable of addressing supervised, unsupervised and, even, semi-supervised hash learning tasks in a natural manner. A series of comparative experiments focused on content-based image retrieval highlights its performance advantages.}, keywords = {Codeword, Hash function learning, support vector machine}, author = {Yinjie Huang and Michael Georgiopoulos and Georgios C. Anagnostopoulos} } @article {56, title = {Multi-Objective Model Selection via Racing}, journal = {Cybernetics, IEEE Transactions on}, volume = {PP}, year = {2015}, pages = {1-1}, abstract = {Model selection is a core aspect in machine learning and is, occasionally, multi-objective in nature. For instance, hyper-parameter selection in a multi-task learning context is of multi-objective nature, since all the tasks{\textquoteright} objectives must be optimized simultaneously. In this paper, a novel multi-objective racing algorithm (RA), namely S-Race, is put forward. Given an ensemble of models, our task is to reliably identify Pareto optimal models evaluated against multiple objectives, while minimizing the total computational cost. As a RA, S-Race attempts to eliminate non-promising models with confidence as early as possible, so as to concentrate computational resources on promising ones. Simultaneously, it addresses the problem of multi-objective model selection (MOMS) in the sense of Pareto optimality. In S-Race, the nonparametric sign test is utilized for pair-wise dominance relationship identification. Moreover, a discrete Holm{\textquoteright}s step-down procedure is adopted to control the family-wise error rate of the set of hypotheses made simultaneously. The significance level assigned to each family is adjusted adaptively during the race. In order to illustrate its merits, S-Race is applied on three MOMS problems: 1) selecting support vector machines for classification; 2) tuning the parameters of artificial bee colony algorithms for numerical optimization; and 3) constructing optimal hybrid recommendation systems for movie recommendation. The experimental results confirm that S-Race is an efficient and effective MOMS algorithm compared to a brute-force approach.}, keywords = {Discrete Holm{\textquoteright}s step-down procedure, model selection, Multi-objective optimization, racing algorithm (RA), sign test (ST)}, issn = {2168-2267}, doi = {10.1109/TCYB.2015.2456187}, author = {Tiantian Zhang and Michael Georgiopoulos and Georgios C. Anagnostopoulos} } @article {48, title = {Multitask Classification Hypothesis Space With Improved Generalization Bounds}, journal = {Neural Networks and Learning Systems, IEEE Transactions on}, volume = {26}, year = {2015}, pages = {1468-1479}, abstract = {This paper presents a pair of hypothesis spaces (HSs) of vector-valued functions intended to be used in the context of multitask classification. While both are parameterized on the elements of reproducing kernel Hilbert spaces and impose a feature mapping that is common to all tasks, one of them assumes this mapping as fixed, while the more general one learns the mapping via multiple kernel learning. For these new HSs, empirical Rademacher complexity-based generalization bounds are derived, and are shown to be tighter than the bound of a particular HS, which has appeared recently in the literature, leading to improved performance. As a matter of fact, the latter HS is shown to be a special case of ours. Based on an equivalence to Group-Lasso type HSs, the proposed HSs are utilized toward corresponding support vector machine-based formulations. Finally, experimental results on multitask learning problems underline the quality of the derived and validate this paper{\textquoteright}s analysis.}, keywords = {Context, Hilbert space, Kernel, Learning systems, Machine learning, pattern recognition, statistical learning, supervised learning, Support vector machines, Training, Upper bound}, issn = {2162-237X}, doi = {10.1109/TNNLS.2014.2347054}, author = {Cong Li and Michael Georgiopoulos and Georgios C. Anagnostopoulos} } @conference {58, title = {Multi-Task Learning with Group-Specific Feature Space Sharing}, booktitle = {Machine Learning and Knowledge Discovery in Databases - ECML/PKDD 2015}, series = {Lecture Notes in Computer Science}, volume = {9285}, year = {2015}, note = {Acceptance rate 23.4\% (89/380).}, pages = {120-136}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {When faced with learning a set of inter-related tasks from a limited amount of usable data, learning each task independently may lead to poor generalization performance. (MTL) exploits the latent relations between tasks and overcomes data scarcity limitations by co-learning all these tasks simultaneously to offer improved performance. We propose a novel Multi-Task Multiple Kernel Learning framework based on Support Vector Machines for binary classification tasks. By considering pair-wise task affinity in terms of similarity between a pair{\textquoteright}s respective feature spaces, the new framework, compared to other similar MTL approaches, offers a high degree of flexibility in determining how similar feature spaces should be, as well as which pairs of tasks should share a common feature space in order to benefit overall performance. The associated optimization problem is solved via a block coordinate descent, which employs a consensus-form Alternating Direction Method of Multipliers algorithm to optimize the Multiple Kernel Learning weights and, hence, to determine task affinities. Empirical evaluation on seven data sets exhibits a statistically significant improvement of our framework{\textquoteright}s results compared to the ones of several other Clustered Multi-Task Learning methods.}, doi = {10.1007/978-3-319-23525-7_8}, author = {Niloofar Yousefi and Michael Georgiopoulos and Georgios C. Anagnostopoulos} } @article {30, title = {Pareto-Path Multitask Multiple Kernel Learning}, journal = {Neural Networks and Learning Systems, IEEE Transactions on}, volume = {26}, year = {2015}, pages = {51-61}, chapter = {51}, abstract = {

A traditional and intuitively appealing Multitask Multiple Kernel Learning (MT-MKL) method is to optimize the sum (thus, the average) of objective functions with (partially) shared kernel function, which allows information sharing among the tasks. We point out that the obtained solution corresponds to a single point on the Pareto Front (PF) of a multiobjective optimization problem, which considers the concurrent optimization of all task objectives involved in the (MTL) problem. Motivated by this last observation and arguing that the former approach is heuristic, we propose a novel support vector machine MT-MKL framework that considers an implicitly defined set of conic combinations of task objectives. We show that solving our framework produces solutions along a path on the aforementioned PF and that it subsumes the optimization of the average of objective functions as a special case. Using the algorithms we derived, we demonstrate through a series of experimental results that the framework is capable of achieving a better classification performance, when compared with other similar MTL approaches.

}, keywords = {Machine learning, optimization methods, pattern recognition, supervised learning, support vector machines (SVM).}, issn = {2162-237X}, doi = {10.1109/TNNLS.2014.2309939}, author = {Cong Li and Michael Georgiopoulos and Georgios C. Anagnostopoulos} } @conference {55, title = {SPRINT Multi-Objective Model Racing}, booktitle = {Proceedings of the 2015 on Genetic and Evolutionary Computation Conference (GECCO {\textquoteright}15) }, year = {2015}, note = {Acceptance rate 36\% (182/505).}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, keywords = {model selection, Multi-objective optimization, racing algorithm, sequential probability ratio test}, isbn = {978-1-4503-3472-3}, doi = {10.1145/2739480.2754791}, url = {http://doi.acm.org/10.1145/2739480.2754791}, author = {Tiantian Zhang and Michael Georgiopoulos and Georgios C. Anagnostopoulos} } @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} } @conference {33, title = {Online model racing based on extreme performance.}, booktitle = {Genetic and Evolutionary Computation Conference, (GECCO {\textquoteright}14), Vancouver, BC, Canada, July 12-16, 2014}, year = {2014}, note = {

Nominated for Best Paper Award.

Acceptance rate 33\% (180/544).

}, publisher = {Association for Computing Machinery (ACM)}, organization = {Association for Computing Machinery (ACM)}, address = {Vancouver, BC, Canada}, abstract = {

Racing algorithms are often used for offline model selection, where models are compared in terms of their average performance over a collection of problems. In this paper, we present a new racing algorithm variant, Max-Race, which makes decisions based on the maximum performance of models. It is an online algorithm, whose goal is to optimally allocate computational resources in a portfolio of evolutionary algorithms, while solving a particular problem instance. It employs a hypothesis test based on extreme value theory in order to decide, which component algorithms to retire, while avoiding unnecessary computations. Experimental results confirm that Max-Race is able to identify the best individual with high precision and low computational overhead. When used as a scheme to select the best from a portfolio of algorithms, the results compare favorably to the ones of other popular algorithm portfolio approaches.

}, doi = {10.1145/2576768.2598336}, url = {http://doi.acm.org/10.1145/2576768.2598336}, author = {Tiantian Zhang and Michael Georgiopoulos and Georgios C. Anagnostopoulos}, editor = {Dirk V. Arnold} } @conference {36, title = {Kernel-based Distance Metric Learning in the Output Space}, booktitle = {Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN)}, year = {2013}, note = {

Nominated for Best Paper Award.

Acceptance rate 72\% (435/605).

}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, organization = {Institute of Electrical and Electronics Engineers (IEEE)}, address = {Dallas, Texas, USA}, abstract = {

In this paper we present two related, kernel-based Distance Metric Learning (DML) methods. Their respective models non-linearly map data from their original space to an output space, and subsequent distance measurements are performed in the output space via a Mahalanobis metric. The dimensionality of the output space can be directly controlled to facilitate the learning of a low-rank metric. Both methods allow for simultaneous inference of the associated metric and the mapping to the output space, which can be used to visualize the data, when the output space is 2-or 3-dimensional. Experimental results for a collection of classification tasks illustrate the advantages of the proposed methods over other traditional and kernel-based DML approaches.

}, doi = {10.1109/IJCNN.2013.6706862}, author = {Cong Li and Michael Georgiopoulos and Georgios C. Anagnostopoulos} } @conference {37, title = {Reduced-Rank Local Distance Metric Learning}, booktitle = {Machine Learning and Knowledge Discovery in Databases - ECML/PKDD 2013}, year = {2013}, note = {

Acceptance rate 25\% (111/443).

}, publisher = {Springer}, organization = {Springer}, address = {Prague, Czech Republic}, abstract = {

We propose a new method for local metric learning based on a conical combination of Mahalanobis metrics and pair-wise similarities between the data. Its formulation allows for controlling the rank of the metrics{\textquoteright} weight matrices. We also offer a convergent algorithm for training the associated model. Experimental results on a collection of classification problems imply that the new method may offer notable performance advantages over alternative metric learning approaches that have recently appeared in the literature.

}, doi = {10.1007/978-3-642-40994-3_15}, author = {Yinjie Huang and Cong Li and Michael Georgiopoulos and Georgios C. Anagnostopoulos} } @conference {35, title = {S-Race: A Multi-Objective Racing Algorithm}, booktitle = {Genetic \& Evolutionary Computation Conference (GECCO)}, year = {2013}, note = {

Finalist for Best Paper Award.

Acceptance rate 36\% (204/570)

}, publisher = {Association for Computing Machinery (ACM)}, organization = {Association for Computing Machinery (ACM)}, address = {Amsterdam, The Netherlands}, abstract = {

This paper presents a multi-objective racing algorithm, S-Race, which efficiently addresses multi-objective model selection problems in the sense of Pareto optimality. As a racing algorithm, S-Race attempts to eliminate candidate models as soon as there is sufficient statistical evidence of their inferiority relative to other models with respect to all objectives. This approach is followed in the interest of controlling the computational effort. S-Race adopts a non-parametric sign test to identify pair-wise domination relationship between models. Meanwhile, Holm{\textquoteright}s Step-Down method is employed to control the overall family-wise error rate of simultaneous hypotheses testing during the race. Experimental results involving the selection of superior Support Vector Machine classifiers according to 2 and 3 performance criteria indicate that S-Race is an efficient and effective algorithm for automatic model selection, when compared to a brute-force, multi-objective selection approach.

}, doi = {10.1145/2463372.2463561}, author = {Tiantian Zhang and Michael Georgiopoulos and Georgios C. Anagnostopoulos}, editor = {Christian Blum and Enrique Alba} } @article {31, title = {A Unifying Framework for Typical Multitask Multiple Kernel Learning Problems}, journal = {Neural Networks and Learning Systems, IEEE Transactions on}, volume = {25}, year = {2013}, month = {July}, pages = {1287-1297}, abstract = {

Over the past few years, multiple kernel learning (MKL) has received significant attention among data-driven feature selection techniques in the context of kernel-based learning. MKL formulations have been devised and solved for a broad spectrum of machine learning problems, including multitask learning (MTL). Solving different MKL formulations usually involves designing algorithms that are tailored to the problem at hand, which is, typically, a nontrivial accomplishment. In this paper we present a general multitask multiple kernel learning (MT-MKL) framework that subsumes well-known MT-MKL formulations, as well as several important MKL approaches on single-task problems. We then derive a simple algorithm that can solve the unifying framework. To demonstrate the flexibility of the proposed framework, we formulate a new learning problem, namely partially-shared common space MT-MKL, and demonstrate its merits through experimentation.

}, keywords = {Algorithm design and analysis, Closed-form solutions, Kernel, Learning systems, Machine learning, Optimization, optimization methods, pattern recognition, supervised learning, Support vector machines, support vector machines (SVMs), support vector machines (SVMs)., Vectors}, issn = {2162-237X}, doi = {10.1109/TNNLS.2013.2291772}, author = {Cong Li and Michael Georgiopoulos and Georgios C. Anagnostopoulos} } @conference {45, title = {A Winner-take-all Methodology: Finding the Best Evolutionary Algorithm for the Global Optimization of Functions}, booktitle = {Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation}, year = {2012}, note = {

Acceptance rate 37\% (172/467).

}, publisher = {ACM}, organization = {ACM}, address = {Philadelphia, Pennsylvania, USA}, keywords = {differential evolution, particle swarm optimization, single-objective optimization}, isbn = {978-1-4503-1178-6}, doi = {10.1145/2330784.2331021}, url = {http://doi.acm.org/10.1145/2330784.2331021}, author = {Tiantian Zhang and Giselle Borrero and Michael Georgiopoulos} } @conference {41, title = {Accelerated learning of Generalized Sammon Mappings}, booktitle = {Neural Networks (IJCNN), The 2011 International Joint Conference on}, year = {2011}, note = {

Acceptance rate 75\% (468/620).

}, month = {July}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, organization = {Institute of Electrical and Electronics Engineers (IEEE)}, address = {San Jose, California, USA }, abstract = {

The Sammon Mapping (SM) has established itself as a valuable tool in dimensionality reduction, manifold learning, exploratory data analysis and, particularly, in data visualization. The SM is capable of projecting high-dimensional data into a low-dimensional space, so that they can be visualized and interpreted. This is accomplished by representing inter-sample dissimilarities in the original space by Euclidean inter-sample distances in the projection space. Recently, Kernel Sammon Mapping (KSM) has been shown to subsume the SM and a few other related extensions to SM. Both of the aforementioned models feature a set of linear weights that are estimated via Iterative Majorization (IM). While IM is significantly faster than other standard gradient-based methods, tackling data sets of larger than moderate sizes becomes a challenging learning task, as IM{\textquoteright}s convergence significantly slows down with increasing data set cardinality. In this paper we derive two improved training algorithms based on Successive Over-Relaxation (SOR) and Parallel Tangents (PARTAN) acceleration, that, while still being first-order methods, exhibit faster convergence than IM. Both algorithms are relatively easy to understand, straightforward to implement and, performance-wise, are as robust as IM. We also present comparative results that illustrate their computational advantages on a set of benchmark problems.

}, keywords = {accelerated learning, Acceleration, Convergence, data visualization, dimensionality reduction, Euclidean inter-sample distances, exploratory data analysis, generalized Sammon mappings, inter-sample dissimilarities, iterative majorization, Kernel, kernel Sammon mapping, learning (artificial intelligence), manifold learning, parallel tangents acceleration, projection space, Prototypes, standard gradient-based methods, Stress, successive over-relaxation, Training}, doi = {10.1109/IJCNN.2011.6033609}, author = {Yinjie Huang and Michael Georgiopoulos and Georgios C. Anagnostopoulos} } @article {38, title = {Efficient Revised Simplex Method for SVM Training}, journal = {Neural Networks, IEEE Transactions on}, volume = {22}, year = {2011}, month = {Oct.}, pages = {1650-1661}, abstract = {

Existing active set methods reported in the literature for support vector machine (SVM) training must contend with singularities when solving for the search direction. When a singularity is encountered, an infinite descent direction can be carefully chosen that avoids cycling and allows the algorithm to converge. However, the algorithm implementation is likely to be more complex and less computationally efficient than would otherwise be required for an algorithm that does not have to contend with the singularities. We show that the revised simplex method introduced by Rusin provides a guarantee of nonsingularity when solving for the search direction. This method provides for a simpler and more computationally efficient implementation, as it avoids the need to test for rank degeneracies and also the need to modify factorizations or solution methods based upon those rank degeneracies. In our approach, we take advantage of the guarantee of nonsingularity by implementing an efficient method for solving the search direction and show that our algorithm is competitive with SVM-QP and also that it is a particularly effective when the fraction of nonbound support vectors is large. In addition, we show competitive performance of the proposed algorithm against two popular SVM training algorithms, SVMLight and LIBSVM.

}, keywords = {active set method, learning (artificial intelligence), LIBSVM algorithm, quadratic programming, rank degeneracy, set theory, simplex method, support vector machine, Support vector machines, SVM training, SVMLight algorithm}, issn = {1045-9227}, doi = {10.1109/TNN.2011.2165081}, author = {Christopher G. Sentelle and Georgios C. Anagnostopoulos and Michael Georgiopoulos} } @conference {39, title = {Kernel principal subspace Mahalanobis distances for outlier detection}, booktitle = {Neural Networks (IJCNN), The 2011 International Joint Conference on}, year = {2011}, note = {

Acceptance rate 75\% (468/620).

}, month = {July}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, organization = {Institute of Electrical and Electronics Engineers (IEEE)}, address = {San Jose, California, USA }, abstract = {

Over the last few years, Kernel Principal Component Analysis (KPCA) has found several applications in outlier detection. A relatively recent method uses KPCA to compute the reconstruction error (RE) of previously unseen samples and, via thresholding, to identify atypical samples. In this paper we propose an alternative method, which performs the same task, but considers Mahalanobis distances in the orthogonal complement of the subspace that is utilized to compute the reconstruction error. In order to illustrate its merits, we provide qualitative and quantitative results on both artificial and real datasets and we show that it is competitive, if not superior, for several outlier detection tasks, when compared to the original RE-based variant and the One-Class SVM detection approach.

}, keywords = {Image reconstruction, Kernel, kernel principal component analysis, Mahalanobis distance, Manifolds, orthogonal subspace complement, outlier detection, principal component analysis, reconstruction error, Single photon emission computed tomography, Support vector machines, Training}, doi = {10.1109/IJCNN.2011.6033548}, author = {Cong Li and Michael Georgiopoulos and Georgios C. Anagnostopoulos} } @article {46, title = {Learning in the Feed-forward Random Neural Network: A Critical Review}, journal = {Perform. Eval.}, volume = {68}, year = {2011}, pages = {361{\textendash}384}, keywords = {ART, CART, Error functions, Evolutionary neural networks, Gradient descent, Learning, Multi-layer perceptron, Multi-objective optimization, Random neural network, SVM}, issn = {0166-5316}, doi = {10.1016/j.peva.2010.07.006}, url = {http://dx.doi.org/10.1016/j.peva.2010.07.006}, author = {Michael Georgiopoulos and Cong Li and Taskin Kocak} } @conference {49, title = {Multi-objective Evolutionary Optimization of Exemplar-based Classifiers: A PNN Test Case}, booktitle = {The 2011 International Joint Conference on Neural Networks (IJCNN{\textquoteright}11) }, year = {2011}, note = {

Acceptance rate 75\% (468/620).

}, publisher = {IEEE}, organization = {IEEE}, address = {San Jose, CA}, abstract = {

In this paper the major principles to effectively design a parameter-less, multi-objective evolutionary algorithm that optimizes a population of probabilistic neural network (PNN) classifier models are articulated; PNN is an example of an exemplar-based classifier. These design principles are extracted from experiences, discussed in this paper, which guided the creation of the parameter-less multi-objective evolutionary algorithm, named MO-EPNN (multi-objective evolutionary probabilistic neural network). Furthermore, these design principles are also corroborated by similar principles used for an earlier design of a parameter-less, multi-objective genetic algorithm used to optimize a population of ART (adaptive resonance theory) models, named MO-GART (multi-objective genetically optimized ART); the ART classifier model is another example of an exemplar-based classifier model. MO-EPNN{\textquoteright}s performance is compared to other popular classifier models, such as SVM (Support Vector Machines) and CART (Classification and Regression Trees), as well as to an alternate competitive method to genetically optimize the PNN. These comparisons indicate that MO-EPNN{\textquoteright}s performance (generalization on unseen data and size) compares favorably to the aforementioned classifier models and to the alternate genetically optimized PNN approach. MO-EPPN{\textquoteright}s good performance, and MO-GART{\textquoteright}s earlier reported good performance, both of whose design relies on the same principles, gives credence to these design principles, delineated in this paper.

}, doi = {10.1109/IJCNN.2011.6033432}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6033432\&tag=1}, author = {Talitha Rubio and Tiantian Zhang and Michael Georgiopoulos and Assem Kaylani} } @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} }