Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Centroid-based text classification is one of the most popular supervised approaches to classify texts into a set of pre-defined classes. Based on the vector-space model, the performance of this classification particularly depends on the way to ight and select important terms in documents for constructing a prototype class vector for each class. In the past, it was shown that term ighting using statistical term distributions could improve classification accuracy. Hover, for different data sets, the best ighting systems are different. Towards this problem,  propose a method that uses homogenous centroid-based classification. The effectiveness of this approach is explored using four data sets. Two main factors are taken into account: model selection and score combination. By experiments, the results show that our system can improve the classification accuracy up to 7.5-8.5% compared to k-NN classifier, 3.7-4.0% compared with the naive Bayes classifier and 1.6-2.7% over the best single-model classification method (p<0.05).
['Verayuth Lertnattee', 'Thanaruk Theeramunkong']
I am writing a research paper on a idea Combining homogeneous classifiers for centroid-based text classification
[{"from": "human", "value": "I am writing a research paper on a idea Combining homogeneous classifiers for centroid-based text classification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Centroid-based text classification is one of the most popular supervised approaches to classify texts into a set of pre-defined classes. Based on the vector-space model, the performance of this classification particularly depends on the way to ight and select important terms in documents for constructing a prototype class vector for each class. In the past, it was shown that term ighting using statistical term distributions could improve classification accuracy. Hover, for different data sets, the best ighting systems are different. Towards this problem,  propose a method that uses homogenous centroid-based classification. The effectiveness of this approach is explored using four data sets. Two main factors are taken into account: model selection and score combination. By experiments, the results show that our system can improve the classification accuracy up to 7.5-8.5% compared to k-NN classifier, 3.7-4.0% compared with the naive Bayes classifier and 1.6-2.7% over the best single-model classification method (p<0.05)."}]