Particle swarm and bayesian networks applied to attribute selection for protein functional classification

The Discrete Particle Swarm (DPSO) algorithm is an optimizationmethod that belongs to the fertile paradigm of Swarm Intelligence. The DPSO was designed for the task of attribute selection and it deals with discrete variables in a straightforward manner. This work extends the DPSO algorithm in two ways. First, we enable the DPSO to select attributes for a Bayesian network algorithm, which is a much more sophisticated algorithm than the Naive Bayes classifier previously used by this algorithm. Second, we apply the DPSO to a challenging protein functional classification data set, involving a large number of classes to be predicted. The performance of the DPSO is compared to the performance of a Binary PSO on the task of selecting attributes in this challenging data set. The criteria used for comparison are: (1) maximizing predictive accuracy; and (2) finding the smallest subset of attributes.

[1]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[2]  T. Krink,et al.  Extending particle swarm optimisers with self-organized criticality , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[3]  Alex Alves Freitas,et al.  Predicting post-synaptic activity in proteins with data mining , 2005, ECCB/JBI.

[4]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[5]  Yan Su,et al.  A Particle Swarm Optimisation Approach in the Construction of Optimal Risky Portfolios , 2005, Artificial Intelligence and Applications.

[6]  Marius M. Solomon,et al.  Algorithms for the Vehicle Routing and Scheduling Problems with Time Window Constraints , 1987, Oper. Res..

[7]  Finn V. Jensen,et al.  Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.

[8]  Dr. Alex A. Freitas.  Data Mining and Knowledge Discovery with Evolutionary Algorithms , 2002, Natural Computing Series.

[9]  Alex Alves Freitas,et al.  A Genetic Algorithm for Solving a Capacitated p-Median Problem , 2004, Numerical Algorithms.

[10]  Pedro Larrañaga,et al.  Globally Multimodal Problem Optimization Via an Estimation of Distribution Algorithm Based on Unsupervised Learning of Bayesian Networks , 2005, Evolutionary Computation.

[11]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[12]  Martin Middendorf,et al.  A Hierarchical Particle Swarm Optimizer for Dynamic Optimization Problems , 2004, EvoWorkshops.

[13]  James Kennedy,et al.  Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[14]  Remco R. Bouckaert,et al.  Properties of Bayesian Belief Network Learning Algorithms , 1994, UAI.

[15]  Alex A. Freitas,et al.  HIERARCHICAL CLASSIFICATION OF G-PROTEIN-COUPLED RECEPTORS WITH A PSO/ACO ALGORITHM , 2006 .

[16]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[17]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[18]  Jürgen Branke,et al.  Multi-swarm Optimization in Dynamic Environments , 2004, EvoWorkshops.

[19]  David J. Spiegelhalter,et al.  Local computations with probabilities on graphical structures and their application to expert systems , 1990 .

[20]  Riccardo Poli,et al.  Exploring extended particle swarms: a genetic programming approach , 2005, GECCO '05.

[21]  Ian Witten,et al.  Data Mining , 2000 .

[22]  Alex Alves Freitas,et al.  A new discrete particle swarm algorithm applied to attribute selection in a bioinformatics data set , 2006, GECCO.