Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A new approach for classification problems, called proximal bilateral-ighted fuzzy support vector machine, is proposed wherein each input example is treated as belonging to both positive and negative classes with different fuzzy memberships. The assumption of treating every input example belonging to both the classes is very ll justified in real world applications. For example, for the study of credit risk assessment a customer can not always be assumed to be absolutely good or bad as he may default or pay his debit at times and therefore he may be treated as belonging to both the classes. Our formulation leads to solving a system of linear equations of size equals to the number of input examples. Computational results of the proposed method on publicly available datasets including two credit risk analysis datasets to that of the standard, proximal and bilateral-ighted fuzzy support vector machine methods clearly demonstrates its efficiency and usefulness.
['S. Balasundaram', 'M. Tanveer']
I am writing a research paper on a idea On proximal bilateral-weighted fuzzy support vector machine classifiers
[{"from": "human", "value": "I am writing a research paper on a idea On proximal bilateral-weighted fuzzy support vector machine classifiers"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A new approach for classification problems, called proximal bilateral-ighted fuzzy support vector machine, is proposed wherein each input example is treated as belonging to both positive and negative classes with different fuzzy memberships. The assumption of treating every input example belonging to both the classes is very ll justified in real world applications. For example, for the study of credit risk assessment a customer can not always be assumed to be absolutely good or bad as he may default or pay his debit at times and therefore he may be treated as belonging to both the classes. Our formulation leads to solving a system of linear equations of size equals to the number of input examples. Computational results of the proposed method on publicly available datasets including two credit risk analysis datasets to that of the standard, proximal and bilateral-ighted fuzzy support vector machine methods clearly demonstrates its efficiency and usefulness."}]