Derong Liu, Chin-Teng Lin, Kay Chen Tan, Graham Kendall, and Angelo Cangelosi

14 IEEE ComputatIonal IntEllIgEnCE magazInE | may 2015

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IEEE Transactions on Neural Networks and Learning Systems

Pareto-Path Multitask Multiple Kernel Learning, by C. Li, M. Georgiopoulos, and G.C. Anagnostopoulos, IEEE Transactions on Neural Networks and Learning Systems, Vol. 26, No. 1, Janu-ary 2015, pp. 51–61.

Digital Object Identifier: 10.1109/ TNNLS.2014.2309939

“A traditional and intuitively appeal-ing multitask multiple kernel learning (MT-MKL) method is developed to opti-mize the sum and the average of objec-tive functions with partially shared kernel function, which allows information shar-ing among the tasks. The obtained solu-tion 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 multitask learning (MTL) problem. A novel sup-port vector machine MT-MKL frame-work is proposed that considers an implicitly defined set of conic combina-tions of task objectives. It is shown that solving this framework produces solu-tions 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 derived, it is demonstrated through a series of experimental results that the framework is capable of achieving a better classifica-

tion performance, when compared with other similar MTL approaches.”

A Parametric Classification Rule Based on the Exponentially Embedded Family, by B. Tang, H. He, Q. Ding, and S. Kay, IEEE Transactions on Neural Net-works and Learning Systems, Vol. 26, No. 2, February 2015, pp. 367–377.

Digital Object Identifier: 10.1109/ TNNLS.2014.2383692

“An approach for estimating model order and constructing probability den-sity function called exponentially embedded family (EEF) is extended to multivariate pattern recognition. Specifi-cally, a parametric classifier rule based on the EEF is developed, in which a distribution is con-structed for each class based on a reference distribution. The proposed method can address different types of classification problems in either a data-driven manner or a model-driven manner. The effectiveness is demon-strated with examples of synthetic data classification and real-life data classifica-tion in a data-driven manner and an example of power quality distur-bance classification in a model-driven manner. To evaluate the classification performance of the approach, the Monte-Carlo method is used in the experiments. The promising experimen-tal results indicate many potential appli-cations of the proposed method.”

IEEE Transactions on Fuzzy Systems

A Collaborative Fuzzy Clustering Algo-rithm in Distributed Network Environ-ments, by J. Zhou, C.L.P. Chen, L. Chen, and H.X. Li, IEEE Transactions on Fuzzy Systems, Vol. 22, No. 6, December 2014, pp. 1443–1456.

Digital Object Identifier: 10.1109/ TFUZZ.2013.2294205

“Traditional centralized approaches using in data clustering possessing pri-vacy and security demands or technical constraints in a large dynamic distributed peer-to-peer network are difficult to sort out. In a P2P network, each peer has equal functionality. A peer is a facilitator

and a worker at the same time. Each peer can commu-nicate with others for the network structure. The local data in this peer and the neces sar y infor mat ion exchanged from others have to be taken into consider-ation when the P2P distrib-uted clustering algorithm intends to complete the locally optimized clusters at each peer. Therefore, the authors propose a novel col-

laborative fuzzy clustering algorithm for prevailing over a distributed P2P net-work. This manner searches the opti-mized clusters at each peer by collabo-rating with topologically neighboring peers only step by step, till it reaches the global consensus of all peers. Further-more, the proposed algorithm can also

Digital Object Identifier 10.1109/MCI.2015.2405275Date of publication: 10 April 2015

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may 2015 | IEEE ComputatIonal IntEllIgEnCE magazInE 15

conduct high-dimensional sparse data clustering and “nonspherical”-shaped data clustering, which are not considered by other distributed methods but widely used in some practical applications. Finally, this study provides several syn-thetic and real-world datasets to demon-strate their efficiency and superiority compared to some existing methods.

Construction of Neurofuzzy Models For Imbalanced Data Classification, by M. Gao, X. Hong, and C.J. Harris, IEEE Transactions on Fuzzy Systems, Vol. 22, No. 6, Dec. 2014, pp. 1472–1488.

Digital Object Identifier: 10.1109/ TFUZZ.2013.2296091

“The authors propose a new class of neurofuzzy construction algorithms with the aim of maximizing generaliza-tion capability specifically for imbal-anced data classification problems based on leave-one-out (LOO) cross-valida-tion. The proposed algorithms are in two stages: First, an initial rule base is constructed based on estimating the Gaussian mixture model with analysis of variance decomposition from input data; the second stage carries out the joint weighted least squares parameter estimation and rule selection using an orthogonal forward subspace selection (OFSS) procedure. The authors show how different LOO based rule selec-tion criteria can be incorporated with OFSS and advocate either maximizing the LOO area under curve of the receiver operating characteristics or maximizing the LOO F-measure if the datasets exhibit imbalanced class distri-bution. Extensive comparative simula-tions illustrate the effectiveness of the proposed algorithms.”

IEEE Transactions on Evolutionary Computation

Reusing Genetic Programming for Ensemble Selection in Classification of Unbalanced Data, by U. Bhowan, M. Johnston, M. Zhang, and X. Yao, IEEE Transactions on Evolutionary Computation, Vol. 18, No. 6, Decem-ber 2014, pp. 893–908.

Digital Object Identifier: 10.1109/ TEVC.2013.2293393

“Classification algorithms can suffer from performance degradation when the class distribution is unbalanced. This paper develops a two-step approach to evolving ensembles using genetic pro-gramming (GP) for unbalanced data. It combines multiple Pareto-approximated front members into a single composite genetic program solution to represent the (optimized) ensemble. It is shown that the proposed GP approach evolves smaller more diverse ensembles com-pared to an established ensemble selec-tion algorithm, while still performing as well as, or better than the established approach. The evolved GP ensembles also perform well compared to other bagging and boosting approaches, particularly on tasks with high levels of class imbalance.”

Learning Value Functions in Interactive Evolutionary Multiobjective Optimiza-tion, by J. Branke, S. Greco, R. Slow-inski, and R. Zielniewicz, IEEE Transactions on Evolutionary Computa-tion, Vol. 19, No. 1, February 2015, pp. 88–102.

Digital Object Identifier: 10.1109/ TEVC.2014.2303783

“This paper proposes an interactive multiobjective evolutionary algorithm (MOEA) that attempts to learn a value function capturing the users’ true prefer-ences. At regular intervals, the user is asked to rank a single pair of solutions. This infor-mation is used to update the algorithm’s internal value function model, and the model is used in subsequent generations to rank solutions incomparable according to dominance. This speeds up evolution to-ward the region of the Pareto front that is most desirable to the user. It takes into ac-count the most general additive value func-tion as a preference model and empirically compares different ways to identify the value function that seems to be the most representative with respect to the given preference information, different types of user preferences, and different ways to use the learned value function in the MOEA. Results on a number of different scenarios suggest that the proposed algorithm works

well over a range of benchmark problems and types of user preferences.”

IEEE Transactions on Computational Intelligence and AI in Games

A Neuroevolution Approach to General Atari Game Playing, by M. Haus-knecht, J. Lehman, R. Miikkilainen and P. Stone, IEEE Transactions on Computational Intelligence and AI in Games, Vol. 6, No. 4, December 2014, pp. 355–366.

Digital Object Identifier: 10.1109/ TCIAIG.2013.2294713

“This paper addresses the challenge of learning to play many different video games with little domain-specific knowl-edge. Specifically, it introduces a neuroevo-lution approach to general Atari 2600 game playing. Four neuroevolution algorithms were paired with three different state repre-sentations and evaluated on a set of 61 Atari games. The neuroevolution agents represent different points along the spectrum of algo-rithmic sophistication - including weight evolution on topologically fixed neural net-works (conventional neuroevolution), cova-riance matrix adaptation evolution strategy (CMA-ES), neuroevolution of augmenting topologies (NEAT), and indirect network encoding (HyperNEAT). State representa-tions include an object representation of the game screen, the raw pixels of the game screen, and seeded noise (a comparative baseline). Results indicate that direct-encoding methods work best on compact state representations while indirect-encod-ing methods (i.e., HyperNEAT) allow scal-ing to higher dimensional representations (i.e., the raw game screen). Previous approaches based on temporal-difference (TD) learning had trouble dealing with the large state spaces and sparse reward gradi-ents often found in Atari games. Neuroevo-lution ameliorates these problems and evolved policies achieve state-of-the-art results, even surpassing human high scores on three games. These results suggest that neuroevolution is a promising approach to general video game playing (GVGP).”

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52 IEEE ComputatIonal IntEllIgEnCE magazInE | maY 2015

[22] R. Romo, A. Hernández, A. Zainos, C. Brody, and L. Lemus, “Sensing without touching: Psychophysical performance based on cortical microstimulation,” Neu-ron, vol. 26, no. 1, pp. 273–278, 2000.[23] W. Schultz, “Getting formal with dopamine and re-ward,” Neuron, vol. 36, no. 2, pp. 241–263, 2002.[24] Y. Wang, X. Su, R. Huai, and M. Wang, “A telem-etry navigation system for animal-robots,” Robot, vol. 28, no. 2, pp. 183–186, 2006.

[25] L. Bourdev and J. Brandt, “Robust object detection via soft cascade,” in Proc. IEEE Computer Society Conf. Com-puter Vision Pattern Recognition, 2005, vol. 2, pp. 236–243.[26] C. Harris and M. Stephens, “A combined corner and edge detector,” in Proc. Alvey Vision Conf., 1988, vol. 15, p. 50.[27] B. D. Lucas and T. Kanade, “An iterative image reg-istration technique with an application to stereo vision,” in Proc. 7th Int. Joint Conf. Artificial Intelligence, 1981, vol. 81, pp. 674–679.

[28] G. Paxinos and C. Watson, The Rat Brain in Stereotax-ic Coordinates: Hard Cover Edition. New York: Academic, 2006.[29] F. Lucivero and G. Tamburrini, “Ethical monitoring of brain-machine interfaces,” AI Soc., vol. 22, no. 3, pp. 449–460, 2008.

IEEE Transactions on Autonomous Mental Development

A Hierarchical System for a Distributed Representation of the Peripersonal Space of a Humanoid Robot, by A. Antonelli, A. Gibaldi, F. Beuth, A.J. Duran, A. Can-essa, M. Chessa, F. Solari, A.P. del Pobil, F. Hamker, E. Chinellato, and S.P. Sabatini, IEEE Transactions on Autono-mous Mental Development, Vol. 6, No. 4, December 2014, pp. 259–273.

Digital Object Identifier: 10.1109/ TAMD.2014.2332875

“This work demonstrates in a humanoid torso the feasibility of an integrated working representation of the robot’s per ipersonal space. To achieve this goal, the paper presents a cognitive architecture that connects several models inspired by neural cir-cuits of the visual, frontal and posterior parietal cortices of the brain. The out-come of the integration process is a

system that allows the robot to create its internal model and its representation of the surrounding space by interacting with the environment directly, through a mutual adaptation of perception and action. The robot is eventually capable of executing a set of tasks, such as rec-ognizing, gazing and reaching target objects, which can work separately or cooperate for supporting more struc-tured and effective behaviors.”

Publication Spotlight (continued from page 15)

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