Abstract
AbstractIncremental Attribute Learning (IAL) is a feasible approach for solving high-dimensional pattern recognition problems. It gradually trains features one by one. Previous research indicated that supervised machine learning with input attribute ordering can improve classification results. Moreover, input space partitioning can also effectively reduce the interference among features. This study proposed IAL based on Grouped Feature Ordering, which fused feature partitioning with feature ordering. The experimental results show that this approach is not only applicable for pattern classification improvement, but also efficient to reduce interference among features.
Highlights
High-dimensional problems are typically cursed with dimensional disasters in problem solving
A representative of such methods is Incremental Attribute Learning (IAL), which incrementally trains pattern features in one or more size. It has been shown as an applicable approach for solving machine learning problems in regression and classification using Genetic Algorithm (GA) 3, 4, Neural Network (NN) 5, 6, Support Vector Machine (SVM) 7, Particle Swarm Optimization (PSO) 8, Decision Tree 9, and so on
The results indicated that the algorithms based on IAL performed better than ITI in 14 of the 16 datasets
Summary
High-dimensional problems are typically cursed with dimensional disasters in problem solving. A representative of such methods is Incremental Attribute Learning (IAL), which incrementally trains pattern features in one or more size It has been shown as an applicable approach for solving machine learning problems in regression and classification using Genetic Algorithm (GA) 3, 4, Neural Network (NN) 5, 6, Support Vector Machine (SVM) 7, Particle Swarm Optimization (PSO) 8, Decision Tree 9, and so on. These previous studies showed that IAL can exhibit better performance than conventional methods which often train all pattern features in one batch.
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