Message-based bucket brigade: an algorithm for the apportionment of credit problem
EWSL-91 Proceedings of the European working session on learning on Machine learning
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Applications of machine learning and rule induction
Communications of the ACM
Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ensemble learning via negative correlation
Neural Networks
Reinforcement Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature selection for ensembles applied to handwriting recognition
International Journal on Document Analysis and Recognition
Boosting and other ensemble methods
Neural Computation
Class-switching neural network ensembles
Neurocomputing
Knowledge transfer via multiple model local structure mapping
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Switching class labels to generate classification ensembles
Pattern Recognition
UIC'10 Proceedings of the 7th international conference on Ubiquitous intelligence and computing
jMetal: A Java framework for multi-objective optimization
Advances in Engineering Software
Feature Selection with Conjunctions of Decision Stumps and Learning from Microarray Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Location and activity recognition using ewatch: a wearable sensor platform
Ambient Intelligence in Everyday Life
Parsing human motion with stretchable models
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Cross-view action recognition via view knowledge transfer
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Evolutionary multi-objective optimization: a historical view of the field
IEEE Computational Intelligence Magazine
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
Designing classifier fusion systems by genetic algorithms
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Activity classification using realistic data from wearable sensors
IEEE Transactions on Information Technology in Biomedicine
Switching between selection and fusion in combining classifiers: anexperiment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hybrid Multiobjective Evolutionary Design for Artificial Neural Networks
IEEE Transactions on Neural Networks
Novel Layered Clustering-Based Approach for Generating Ensemble of Classifiers
IEEE Transactions on Neural Networks
Hybrid Training Method for MLP: Optimization of Architecture and Training
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Mining actionlet ensemble for action recognition with depth cameras
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Hi-index | 0.01 |
In this paper, we propose a new multi-objective evolutionary algorithm-based ensemble optimizer coupled with neural network models for undertaking feature selection and classification problems. Specifically, the Modified micro Genetic Algorithm (MmGA) is used to form the ensemble optimizer. The aim of the MmGA-based ensemble optimizer is two-fold, i.e. to select a small number of input features for classification and to improve the classification performances of neural network models. To evaluate the effectiveness of the proposed system, a number of benchmark problems are first used, and the results are compared with those from other methods. The applicability of the proposed system to a human motion detection and classification task is then evaluated. The outcome positively demonstrates that the proposed MmGA-based ensemble optimizer is able to improve the classification performances of neural network models with a smaller number of input features.