Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: While hierarchical semi-supervised classification methods have been previously studied,  still lack an algorithm that can learn a non-predefined categorical hierarchy from multi-labeled data at various levels of specificity. Inspired by human psychology and learning experience, in this paper  propose a semi-supervised learning method that can classify multi-labeled data into a hierarchy based on the label's specificity level such that the separability beten each class and its siblings is greater than the separability beten each class and its parents. To build the hierarchy  show that a minimum spanning tree minimizes an upper bound on the pairwise Kullback-Liebler divergence beten the true and approximated distributions.  show the effectiveness of our method using three types of data sets and draw a comparison beten our learned hierarchy and one learned by human subjects using the same data set.  also show the effectiveness of our method compared to hierarchical clustering.
['Ailar Javadi', 'Alexander G. Gray', 'David V. Anderson', 'Visar Berisha']
I am writing a research paper on a idea Semi-supervised hierarchy learning using multiple-labeled data
[{"from": "human", "value": "I am writing a research paper on a idea Semi-supervised hierarchy learning using multiple-labeled data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: While hierarchical semi-supervised classification methods have been previously studied,  still lack an algorithm that can learn a non-predefined categorical hierarchy from multi-labeled data at various levels of specificity. Inspired by human psychology and learning experience, in this paper  propose a semi-supervised learning method that can classify multi-labeled data into a hierarchy based on the label's specificity level such that the separability beten each class and its siblings is greater than the separability beten each class and its parents. To build the hierarchy  show that a minimum spanning tree minimizes an upper bound on the pairwise Kullback-Liebler divergence beten the true and approximated distributions.  show the effectiveness of our method using three types of data sets and draw a comparison beten our learned hierarchy and one learned by human subjects using the same data set.  also show the effectiveness of our method compared to hierarchical clustering."}]