Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Mining with multilabel data is a popular topic in data mining. When performing classification on multilabel data, existing methods using traditional classifiers, such as support vector machines SVMs, k-nearest neighbor k-NN, and decision trees, have relatively poor accuracy and efficiency. Motivated by this,  present a new algorithm adaptation method, namely, a decision tree–based method for multilabel classification in domains with large-scale data sets called decision tree for multi-label classification DTML.  build an incremental decision tree to reduce the learning time and divide the training data and adopt the k-NN classifier at leaves to improve the classification accuracy. Extensive studies show that our algorithm can efficiently learn from multilabel data while maintaining good performance on example-based evaluation metrics compared to nine state-of-the-art multilabel classification methods. Thus,  draw a conclusion that  provide an efficient and effective incremental algorithm adaptation method for multilabel classification especially in domains with large-scale multilabel data.
['Peipei Li', 'Xindong Wu', 'Xuegang Hu', 'Hao Wang']
I am writing a research paper on a idea An Incremental Decision Tree for Mining Multilabel Data
[{"from": "human", "value": "I am writing a research paper on a idea An Incremental Decision Tree for Mining Multilabel Data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Mining with multilabel data is a popular topic in data mining. When performing classification on multilabel data, existing methods using traditional classifiers, such as support vector machines SVMs, k-nearest neighbor k-NN, and decision trees, have relatively poor accuracy and efficiency. Motivated by this,  present a new algorithm adaptation method, namely, a decision tree\u2013based method for multilabel classification in domains with large-scale data sets called decision tree for multi-label classification DTML.  build an incremental decision tree to reduce the learning time and divide the training data and adopt the k-NN classifier at leaves to improve the classification accuracy. Extensive studies show that our algorithm can efficiently learn from multilabel data while maintaining good performance on example-based evaluation metrics compared to nine state-of-the-art multilabel classification methods. Thus,  draw a conclusion that  provide an efficient and effective incremental algorithm adaptation method for multilabel classification especially in domains with large-scale multilabel data."}]