Order Optimization of Evolutionary Hypernetworks Using Genetic Algorithm

Hyper networks consist of a large number of hyper edges that represent high-order features sampled from training sets. The order of hyper edges is an important parameter of a hyper network model and influences the performance of the hyper network classification system. Previous studies determine the parameter by the artificial exhaustive search method before evolutionary learning. Not only is the approach time-consuming, but also the traditional hyper network lacks generalization. In this study, a genetic algorithm is employed to optimize the order of hyper networks. The proposed method is tested on the acute leukemia and the colon cancer dataset. Experimental results show that the proposed approach can find the global optimal order automatically. Also, a comparative study on five classification algorithms shows that the improved hyper network model achieves a comparable classification performance.

[1]  Robert Veroff,et al.  A Bayesian Network Classification Methodology for Gene Expression Data , 2004, J. Comput. Biol..

[2]  U. Alon,et al.  Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[3]  A. Bonato,et al.  Graphs and Hypergraphs , 2022 .

[4]  Byoung-Tak Zhang,et al.  Evolving hypernetworks for pattern classification , 2007, 2007 IEEE Congress on Evolutionary Computation.

[5]  Byoung-Tak Zhang,et al.  Text Classifiers Evolved on a Simulated DNA Computer , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[6]  Chun-Gui Xu,et al.  A genetic programming-based approach to the classification of multiclass microarray datasets , 2009, Bioinform..

[7]  Byoung-Tak Zhang,et al.  A Bayesian Algorithm for In Vitro Molecular Evolution of Pattern Classifiers , 2004, DNA.

[8]  Li Wang,et al.  Hybrid huberized support vector machines for microarray classification and gene selection , 2008, Bioinform..

[9]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[10]  Sung-Bae Cho,et al.  Cancer classification using ensemble of neural networks with multiple significant gene subsets , 2007, Applied Intelligence.

[11]  Byoung-Tak Zhang,et al.  Molecular programming: evolving genetic programs in a test tube , 2005, GECCO '05.

[12]  Byoung-Tak Zhang,et al.  Use of Evolutionary Hypernetworks for Mining Prostate Cancer Data , 2007 .

[13]  Byoung-Tak Zhang,et al.  DNA Hypernetworks for Information Storage and Retrieval , 2006, DNA.