Comparative study of two-layer particle swarm optimization and particle swarm optimization in classification for tumor gene expression data with different dimensionalities
- Resource Type
- Conference
- Authors
- Liu, Yajie; Shi, Xinling; Li, Baolei; Gao, Lian; Gou, Changxing; Zhang, Qinhu; Huang, Yunchao
- Source
- 2013 6th International Conference on Biomedical Engineering and Informatics Biomedical Engineering and Informatics (BMEI), 2013 6th International Conference on. :524-529 Dec, 2013
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Signal Processing and Analysis
Classification algorithms
Tumors
Training
Prediction algorithms
Accuracy
Particle swarm optimization
Gene expression
classification
comparison
tumor
gene
TLPSO
- Language
- ISSN
- 1948-2914
1948-2922
Classification of gene expression data to determine different type or subtype of tumor samples is significantly important to research tumors in molecular biology level. Sample genes (dimensionalities) play a fundamental role in classification. Feature selection technologies used to reduce gene numbers and find informative genes have been presented in recent years. But the performance of feature selection in gene classification research is still controversial. In this study, a classification algorithm based on the two-layer particle swarm optimization (TLPSO) is established to classify the uncertain training sample sets obtained from three gene expression datasets which contain the leukemia, diffuse large B cell lymphoma (DLBCL) and multi-class tumors dataset respectively with the exponential increasing of gene numbers. Compared the results obtained by using the particle swarm optimization (PSO), the classification stability and accuracy of the results based on the proposed TLPSO classification algorithm is improved significantly and more information to clinicians for choosing more appropriate treatment can extracted.