Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Categorisation of digital documents is useful for organisation and retrieval. While document categories can be a set of unstructured category labels, some document categories are hierarchically structured.  investigates automatic hierarchical categorisation and, specifically, the role of features in the development of more effective categorisers.  show that a good hierarchical machine learning-based categoriser can be developed using small numbers of features from pre-categorised training documents. Overall,  show that by using a few terms, categorisation accuracy can be improved substantially: unstructured leaf level categorisation can be improved by up to 8.6%, while top-down hierarchical categorisation accuracy can be improved by up to 12%. In addition, unlike other feature selection models --- which typically require different feature selection parameters for categories at different hierarchical levels --- our technique works equally ll for all categories in a hierarchical structure.  conclude that, in general, more accurate hierarchical categorisation is possible by using our simple feature selection technique.
['Wahyu Wibowo', 'Hugh E. Williams']
I am writing a research paper on a idea Simple and accurate feature selection for hierarchical categorisation
[{"from": "human", "value": "I am writing a research paper on a idea Simple and accurate feature selection for hierarchical categorisation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Categorisation of digital documents is useful for organisation and retrieval. While document categories can be a set of unstructured category labels, some document categories are hierarchically structured.  investigates automatic hierarchical categorisation and, specifically, the role of features in the development of more effective categorisers.  show that a good hierarchical machine learning-based categoriser can be developed using small numbers of features from pre-categorised training documents. Overall,  show that by using a few terms, categorisation accuracy can be improved substantially: unstructured leaf level categorisation can be improved by up to 8.6%, while top-down hierarchical categorisation accuracy can be improved by up to 12%. In addition, unlike other feature selection models --- which typically require different feature selection parameters for categories at different hierarchical levels --- our technique works equally ll for all categories in a hierarchical structure.  conclude that, in general, more accurate hierarchical categorisation is possible by using our simple feature selection technique."}]