Incremental attribute based particle swarm optimization

Bai, Wei; Cheng, Shi; Tadjouddine, Emmanuel M.; Guan, Sheng-Uei

doi:10.1109/icnc.2012.6234699

Cited by 10 publications

(4 citation statements)

References 13 publications

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“…neural network (NN) [8,18], support vector machine (SVM) [19], particle swarm optimization (PSO) [20], decision tree [21], and so on. These previous studies also showed that IAL can exhibit better performance than conventional methods that train all pattern features in one batch.…”

Section: Ial and Feature Orderingmentioning

confidence: 99%

EEG Eye State Identification Using Incremental Attribute Learning with Time-Series Classification

Wang

Guan

Man

et al. 2014

Mathematical Problems in Engineering

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Eye state identification is a kind of common time-series classification problem which is also a hot spot in recent research. Electroencephalography (EEG) is widely used in eye state classification to detect human's cognition state. Previous research has validated the feasibility of machine learning and statistical approaches for EEG eye state classification. This paper aims to propose a novel approach for EEG eye state identification using incremental attribute learning (IAL) based on neural networks. IAL is a novel machine learning strategy which gradually imports and trains features one by one. Previous studies have verified that such an approach is applicable for solving a number of pattern recognition problems. However, in these previous works, little research on IAL focused on its application to time-series problems. Therefore, it is still unknown whether IAL can be employed to cope with time-series problems like EEG eye state classification. Experimental results in this study demonstrates that, with proper feature extraction and feature ordering, IAL can not only efficiently cope with time-series classification problems, but also exhibit better classification performance in terms of classification error rates in comparison with conventional and some other approaches.

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Section: Ial and Feature Orderingmentioning

confidence: 99%

EEG Eye State Identification Using Incremental Attribute Learning with Time-Series Classification

Wang

Guan

Man

et al. 2014

Mathematical Problems in Engineering

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“…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 Algorithms (GA) [3,4], Neural Networks (NN) [5,6], Support Vector Machines (SVM) [7], Particle Swarm Optimization (PSO) [8], Decision Tree [9], and so on. These previous studies also have shown that IAL can exhibit better performance than conventional methods which prefer to train all pattern features in one batch.…”

Section: Introductionmentioning

confidence: 99%

Feature Ordering for Neural Incremental Attribute Learning Based on Fisher's Linear Discriminant

Wang

Guan

2013

2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics

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Incremental attribute learning (IAL) often gradually imports and trains pattern features in one or more size, which makes feature ordering become a novel preprocessing work in IAL process. In previous studies, the calculation of feature ordering is often based on feature's single contribution to outputs, which is similar to wrapper methods in feature selection. However, such a process is time-consuming. In this paper, a new approach for feature ordering is presented, where feature ordering is ranked by Fisher Score, a metric derived by Fisher's Linear Discriminant (FLD). Based on neural network IAL model, experimental results verified that feature ordering derived by Fisher Score can not only save time, but also obtain the best classification rate compared with those in previous studies.

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“…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 also showed that IAL can exhibit better performance than conventional methods which often train all pattern features in one batch.…”

Section: Introductionmentioning

confidence: 99%

Neural Incremental Attribute Learning in Groups

Liu¹,

Wang²,

Guan³

et al. 2015

IJCIS

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Incremental 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.

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Incremental attribute based particle swarm optimization

Cited by 10 publications

References 13 publications

EEG Eye State Identification Using Incremental Attribute Learning with Time-Series Classification

EEG Eye State Identification Using Incremental Attribute Learning with Time-Series Classification

Feature Ordering for Neural Incremental Attribute Learning Based on Fisher's Linear Discriminant

Neural Incremental Attribute Learning in Groups

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