Publicação
Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients
| Resumo: | This paper proposes a modified binary particle swarm optimization (MBPSO) method for feature election with the simultaneous optimization of SVM kernel parameter setting, applied to mortality prediction in septic patients. An enhanced version of binary particle swarm optimization, designed to cope with premature convergence of the BPSO algorithm is proposed. MBPSO control the swarm variability using the velocity and the similarity between best swarm solutions. This paper uses support vector machines in a wrapper approach, where the kernel parameters are optimized at the same time. The approach is applied to predict the outcome (survived or deceased) of patients with septic shock. Further, MBPSO is tested in several benchmark datasets and is compared with other PSO based algorithms and genetic algorithms (GA). The experimental results showed that the proposed approach can correctly select the discriminating input features and also achieve high classification accuracy, specially when compared to other PSO based algorithms. When compared to GA, MBPSO is similar in terms of accuracy, but the subset solutions have less selected features. |
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| Autores principais: | Vieira, Susana M. |
| Outros Autores: | Mendonça, Luís F. Mendonça; Farinha, Gonçalo J.; Sousa, João Miguel da Costa |
| Assunto: | Feature selection Wrapper methods Particle swarm optimization Premature convergence Sepsis Support vector machines |
| Ano: | 2013 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Escola Superior Náutica Infante D. Henrique |
| Idioma: | inglês |
| Origem: | Escola Superior Náutica Infante D. Henrique |
| Resumo: | This paper proposes a modified binary particle swarm optimization (MBPSO) method for feature election with the simultaneous optimization of SVM kernel parameter setting, applied to mortality prediction in septic patients. An enhanced version of binary particle swarm optimization, designed to cope with premature convergence of the BPSO algorithm is proposed. MBPSO control the swarm variability using the velocity and the similarity between best swarm solutions. This paper uses support vector machines in a wrapper approach, where the kernel parameters are optimized at the same time. The approach is applied to predict the outcome (survived or deceased) of patients with septic shock. Further, MBPSO is tested in several benchmark datasets and is compared with other PSO based algorithms and genetic algorithms (GA). The experimental results showed that the proposed approach can correctly select the discriminating input features and also achieve high classification accuracy, specially when compared to other PSO based algorithms. When compared to GA, MBPSO is similar in terms of accuracy, but the subset solutions have less selected features. |
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