Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
Genetic Programming and Evolvable Machines
Feature Space Transformation Using Genetic Algorithms
IEEE Intelligent Systems
Particle swarm based Data Mining Algorithms for classification tasks
Parallel Computing - Special issue: Parallel and nature-inspired computational paradigms and applications
Genetic Programming with a Genetic Algorithm for Feature Construction and Selection
Genetic Programming and Evolvable Machines
Facing classification problems with Particle Swarm Optimization
Applied Soft Computing
Applications of particle swarm optimisation in integrated process planning and scheduling
Robotics and Computer-Integrated Manufacturing
Computers and Industrial Engineering
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
A survey of particle swarm optimization applications in electric power systems
IEEE Transactions on Evolutionary Computation
Concentration based feature construction approach for spam detection
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Genetic programming for attribute construction in data mining
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Feature construction and dimension reduction using genetic programming
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Efficient case based feature construction
ECML'05 Proceedings of the 16th European conference on Machine Learning
Multi-objective particle swarm optimisation (PSO) for feature selection
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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In classification, the quality of the data representation significantly influences the performance of a classification algorithm. Feature construction can improve the data representation by constructing new high-level features. Particle swarm optimisation (PSO) is a powerful search technique, but has never been applied to feature construction. This paper proposes a PSO based feature construction approach (PSOFC) to constructing a single new high-level feature using original low-level features and directly addressing binary classification problems without using any classification algorithm. Experiments have been conducted on seven datasets of varying difficulty. Three classification algorithms (decision trees, naive bayes, and k-nearest neighbours) are used to evaluate the performance of the constructed feature on test set. Experimental results show that a classification algorithm using the single constructed feature often achieves similar (or even better) classification performance than using all the original features, and in almost all cases, adding the constructed feature to the original features significantly improves its classification performance. In most cases, PSOFC as a classification algorithm (using the constructed feature only) achieves better classification performance than a classification algorithm using all the original features, but needs much less computational cost. This paper represents the first study on using PSO for feature construction in classification.