Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Supervised learning is ll-known and widely applied in many domains including bioinformatics, cheminformatics and financial forecasting. Hover, the interference from irrelevant features may lead to the poor accuracy of classifiers. As a popular feature selection model, GA-SVM is desirable in many of those cases to filter out irrelevant features and improve the learning performance subsequently. Hover, the high computational cost strongly discourages the application of GA-SVM in large-scale datasets. , an HPC-enabled GA-SVM (HGA-SVM) is proposed by integrating data parallelization, multithreading and heuristic techniques with the ultimate goal of robustness and low computational cost. Our proposed model is comprised of four improvement strategies: 1) GA Parallelization, 2) SVM Parallelization, 3) Neighbor Search and 4) Evaluation Caching. All the four strategies improve various aspects of the feature selection model and contribute collectively towards higher computational throughput.
['Tianyou Zhang', 'Xiuju Fu', 'Rick Siow Mong Goh', 'Chee Keong Kwoh', 'Gary Kee Khoon Lee']
I am writing a research paper on a idea A GA-SVM feature selection model based on high performance computing techniques
[{"from": "human", "value": "I am writing a research paper on a idea A GA-SVM feature selection model based on high performance computing techniques"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Supervised learning is ll-known and widely applied in many domains including bioinformatics, cheminformatics and financial forecasting. Hover, the interference from irrelevant features may lead to the poor accuracy of classifiers. As a popular feature selection model, GA-SVM is desirable in many of those cases to filter out irrelevant features and improve the learning performance subsequently. Hover, the high computational cost strongly discourages the application of GA-SVM in large-scale datasets. , an HPC-enabled GA-SVM (HGA-SVM) is proposed by integrating data parallelization, multithreading and heuristic techniques with the ultimate goal of robustness and low computational cost. Our proposed model is comprised of four improvement strategies: 1) GA Parallelization, 2) SVM Parallelization, 3) Neighbor Search and 4) Evaluation Caching. All the four strategies improve various aspects of the feature selection model and contribute collectively towards higher computational throughput."}]