Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Boosting is a ll-known method for improving the accuracy of many learning algorithms. ,  propose a novel boosting algorithm, VipBoost (voting on boosting classifications from imputed learning sets), which first generates multiple incomplete datasets from the original dataset by randomly removing a small percentage of observed attribute values, then uses an imputer to fill in the missing values. It then applies AdaBoost (using some base learner) to produce classifiers trained on each of the imputed learning sets, to produce multiple classifiers. The subsequent prediction on a new test case is the most frequent classification from these classifiers. Our empirical results show that VipBoost produces very effective classifiers that significantly improve accuracy for unstable base learners and some stable learners, especially when the initial dataset is incomplete.
['Xiaoyuan Su', 'Taghi M. Khoshgoftaar', 'Russell Greiner']
I am writing a research paper on a idea VipBoost: A More Accurate Boosting Algorithm
[{"from": "human", "value": "I am writing a research paper on a idea VipBoost: A More Accurate Boosting Algorithm"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Boosting is a ll-known method for improving the accuracy of many learning algorithms. ,  propose a novel boosting algorithm, VipBoost (voting on boosting classifications from imputed learning sets), which first generates multiple incomplete datasets from the original dataset by randomly removing a small percentage of observed attribute values, then uses an imputer to fill in the missing values. It then applies AdaBoost (using some base learner) to produce classifiers trained on each of the imputed learning sets, to produce multiple classifiers. The subsequent prediction on a new test case is the most frequent classification from these classifiers. Our empirical results show that VipBoost produces very effective classifiers that significantly improve accuracy for unstable base learners and some stable learners, especially when the initial dataset is incomplete."}]