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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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Towards a national circular economy indicator system in China: an evaluation and critical analysis
Abstract It is widely acknowledged that China’s economic miracle has been achieved at the expense of its natural capital and environment. In order to deal with this problem, the circular economy (CE)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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Evolving Deep Convolutional Neural Networks for Image Classification
TLDR
We propose a new method using genetic algorithms for evolving the architectures and connection weight initialization values of a deep convolutional neural network to address image classification problems. Expand
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Binary particle swarm optimisation for feature selection: A filter based approach
TLDR
Based on binary particle swarm optimisation (BPSO) and information theory, this paper proposes two new filter feature selection methods for classification problems. Expand
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Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification
TLDR
Convolutional neural networks (CNNs) have gained remarkable success on many image classification tasks. Expand
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Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-Based Performance Predictor
TLDR
An end-to-end offline performance predictor based on the random forest is proposed to accelerate the fitness evaluation in EDL. Expand
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Genetic programming for feature construction and selection in classification on high-dimensional data
TLDR
This work presents a comprehensive study to investigate the use of GP for feature construction and selection on high-dimensional classification problems. Expand
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Pareto front feature selection based on artificial bee colony optimization
TLDR
We propose a new multi-objective artificial bee colony algorithm for feature selection and compare its performance on 12 benchmark datasets. Expand
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