Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The advent of microarray technology allows researchers to perform cancer diagnosis on gene level. Hover, the microarray data usually contains a large quantity of irrelevant, noisy and redundant genes which may seriously deteriorate the prediction accuracy. What's more, microarray data always suffers from the problem of a relatively small number of samples, which raises the difficulties in diagnosis. Accordingly, gene selection plays an important role in cancer diagnosis using microarray data. As a state-of-the-art classifier, support vector machine (SVM) has been successfully applied in solving the small-sample recognition problems and therefore is very suitable for microarray data processing. Hover, to improve the recognition accuracy, it is essential to choose certain good enough parameters for SVM in applications. This study proposes a new gene selection method based on a binary version of particle swarm optimisation (BPSO) algorithm and SVM. In the method, the task of gene selection and parameter tuning of SVM is performed simultaneously by BPSO. To verify the proposed method, three benchmark dataset (leukaemia, breast and colon) are studied. The experimental results show that the proposed method is highly effective and competitive compared with other methods.
['Shutao Li', 'Xixian Wu', 'Mingkui Tan']
I am writing a research paper on a idea Gene selection and parameter determination of support vector machines based on BPSO algorithm
[{"from": "human", "value": "I am writing a research paper on a idea Gene selection and parameter determination of support vector machines based on BPSO algorithm"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The advent of microarray technology allows researchers to perform cancer diagnosis on gene level. Hover, the microarray data usually contains a large quantity of irrelevant, noisy and redundant genes which may seriously deteriorate the prediction accuracy. What's more, microarray data always suffers from the problem of a relatively small number of samples, which raises the difficulties in diagnosis. Accordingly, gene selection plays an important role in cancer diagnosis using microarray data. As a state-of-the-art classifier, support vector machine (SVM) has been successfully applied in solving the small-sample recognition problems and therefore is very suitable for microarray data processing. Hover, to improve the recognition accuracy, it is essential to choose certain good enough parameters for SVM in applications. This study proposes a new gene selection method based on a binary version of particle swarm optimisation (BPSO) algorithm and SVM. In the method, the task of gene selection and parameter tuning of SVM is performed simultaneously by BPSO. To verify the proposed method, three benchmark dataset (leukaemia, breast and colon) are studied. The experimental results show that the proposed method is highly effective and competitive compared with other methods."}]