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126
Particle swarm model selection
- JMLR, Special Topic on Model Selection
, 2009
"... This paper proposes the application of particle swarm optimization (PSO) to the problem of full model selection, FMS, for classification tasks. FMS is defined as follows: given a pool of preprocessing methods, feature selection and learning algorithms, to select the combination of these that obtains ..."
Abstract
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Cited by 20 (7 self)
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This paper proposes the application of particle swarm optimization (PSO) to the problem of full model selection, FMS, for classification tasks. FMS is defined as follows: given a pool of preprocessing methods, feature selection and learning algorithms, to select the combination
New Fitness Functions in Binary Particle Swarm Optimisation for Feature Selection
"... Abstract-Feature selection is an important data preprocessing technique in classification problems. This paper proposes two new fitness functions in binary particle swarm optimisation (BPSO) for feature selection to choose a small number of features and achieve high classification accuracy. In the ..."
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Abstract-Feature selection is an important data preprocessing technique in classification problems. This paper proposes two new fitness functions in binary particle swarm optimisation (BPSO) for feature selection to choose a small number of features and achieve high classification accuracy
Gaussian Based Particle Swarm Optimisation and Statistical Clustering for Feature Selection
"... Abstract. Feature selection is an important but difficult task in classification, which aims to reduce the number of features and maintain or even increase the classification accuracy. This paper proposes a new particle swarm optimisation (PSO) algorithm using statistical clustering information to ..."
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Abstract. Feature selection is an important but difficult task in classification, which aims to reduce the number of features and maintain or even increase the classification accuracy. This paper proposes a new particle swarm optimisation (PSO) algorithm using statistical clustering information
Gaussian Transformation based Representation in Particle Swarm Optimisation for Feature Selection
"... Abstract. In classification, feature selection is an important but challenging task, which requires a powerful search technique. Particle swarm optimisation (PSO) has recently gained much attention for solving feature selection problems, but the current representation typically forms a high-dimensio ..."
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Abstract. In classification, feature selection is an important but challenging task, which requires a powerful search technique. Particle swarm optimisation (PSO) has recently gained much attention for solving feature selection problems, but the current representation typically forms a high
On the Effect of Selection and Archiving Operators in Many-Objective Particle Swarm Optimisation
"... The particle swarm optimisation (PSO) heuristic has been used for a number of years now to perform multi-objective optimisation, however its performance on many-objective op-timisation (problems with four or more competing objec-tives) has been less well examined. Many-objective opti-misation is wel ..."
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The particle swarm optimisation (PSO) heuristic has been used for a number of years now to perform multi-objective optimisation, however its performance on many-objective op-timisation (problems with four or more competing objec-tives) has been less well examined. Many-objective opti-misation
Feature Selection based on Rough Sets and Particle Swarm Optimization
"... Abstract: We propose a new feature selection strategy based on rough sets and Particle Swarm Optimization (PSO). Rough sets has been used as a feature selection method with much success, but current hill-climbing rough set approaches to feature selection are inadequate at finding optimal reductions ..."
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Abstract: We propose a new feature selection strategy based on rough sets and Particle Swarm Optimization (PSO). Rough sets has been used as a feature selection method with much success, but current hill-climbing rough set approaches to feature selection are inadequate at finding optimal reductions
Understanding Particle Swarm Optimisation by EVOLVING PROBLEM LANDSCAPES
- IN PROCEEDINGS SIS 2005 IEEE SWARM INTELLIGENCE
, 2005
"... Genetic programming (GP) is used to create fitness landscapes which highlight strengths and weaknesses of different types of PSO and to contrast population-based swarm approaches with non stochastic gradient followers (i.e. hill climbers). These automatically generated benchmark problems yield insig ..."
Abstract
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Cited by 1 (1 self)
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Genetic programming (GP) is used to create fitness landscapes which highlight strengths and weaknesses of different types of PSO and to contrast population-based swarm approaches with non stochastic gradient followers (i.e. hill climbers). These automatically generated benchmark problems yield
Parallel Feature Selection Algorithm based on Rough Sets and Particle Swarm Optimization
"... Abstract—The aim of this paper is to propose a new method of solving feature selection problem. Foundations of presented algorithm lie in the theory of rough sets. Feature selection methods based on rough sets have been used with success in many data mining problems, but their weakness is their comp ..."
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uses simple mathematical operators to update position and velocity of each particle, which makes PSO computationally inexpensive in terms of both memory and runtime. The presented feature selection algorithm treats each feature subset as separate particle. Optimal subset, in terms of selected measure
Feature Selection in Extrusion Beltline Moulding Process Using Particle Swarm Optimization
"... Optimization is necessary for the control of any process to achieve better product quality, high productivity with low cost. The beltline moulding process is difficult task due to its low defects, making the material sensitive to reject. The efficient beltline moulding process involves the optimal s ..."
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to obtain optimum process parameters can be successfully applied to beltline moulding process through Particle Swarm Optimization (PSO). Results obtained are superior in comparison with Genetic Algorithm (GA) approach.
An Improved Particle Swarm Optimization for Feature Selection
"... Particle Swarm Optimization (PSO) is a popular and bionic algorithm based on the social behavior associated with bird flocking for optimization problems. To maintain the diversity of swarms, a few studies of multi-swarm strategy have been reported. However, the competition among swarms, reservation ..."
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consists of a number of sub-swarms and a multi-swarm scheduler that can monitor and control each sub-swarm using the rules. To further settle the feature selection problems, we propose an Improved Feature Selection (IFS) method by integrating MSPSO, Support Vector Machines (SVM) with F-score method
Results 1 - 10
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126
