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Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach
TLDR
We investigate two PSO-based multi-objective feature selection algorithms based on the idea of nondominated sorting into PSO to address feature selection problems. Expand
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A Survey on Evolutionary Computation Approaches to Feature Selection
TLDR
Feature selection is an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of an algorithm. Expand
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Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms
TLDR
We propose three new initialisation strategies and three new personal best and global best updating mechanisms in PSO to develop novel feature selection approaches with the goals of maximising the classification performance, minimising the number of features and reducing the computational time. Expand
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New fitness functions in binary particle swarm optimisation for feature selection
TLDR
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. Expand
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Reusing Building Blocks of Extracted Knowledge to Solve Complex, Large-Scale Boolean Problems
TLDR
A genetic programming like rich encoding scheme has been constructed to identify building blocks of knowledge in a learning classifier system and reuse them to learn more complex large-scale problems in the domain. Expand
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Multi-objective Evolutionary Algorithms for filter Based Feature Selection in Classification
TLDR
Feature selection is a multi-objective problem with the two main conflicting objectives of minimising the number of features and maximising the classification performance. Expand
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Emotion inspired adaptive robotic path planning
TLDR
This paper presents an emotion inspired adaptive path planning approach for autonomous robotic navigation. Expand
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A multi-objective particle swarm optimisation for filter-based feature selection in classification problems
TLDR
We develop two novel multi-objective feature selection frameworks for classification by applying mutual information and entropy as two different filter evaluation criteria in each of the proposed frameworks. Expand
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Multi-objective particle swarm optimisation (PSO) for feature selection
TLDR
This paper proposes two multi-objective algorithms for selecting the Pareto front of non-dominated solutions (feature subsets) for classification based on particle swarm optimisation. Expand
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A Comprehensive Comparison on Evolutionary Feature Selection Approaches to Classification
TLDR
Feature selection is an important data preprocessing step in machine learning and data mining, such as classification tasks. Expand
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