Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  explores the idea that a concept lattice is an in- formation channel beten objects and attributes. For this purpose  study the behaviour of incidences in L-formal contexts where L is the range of an information-theoretic entropy function. Examples of such data abound in machine learning and data mining, e.g. confusion matri- ces of multi-class classifiers or document-term matrices.  use a ll- motivated information-theoretic heuristic, the maximization of mutual information, that in our conclusions provides a flavour of feature selection providing and information-theory explanation of an established practice in Data Mining, Natural Language Processing and Information Retrieval applications, viz. stop-wording and frequency thresholding.  also in- troduce a post-clustering class identification in the presence of confusions and a flavour of term selection for a multi-label document classification task.
['Francisco José Valverde Albacete', 'Carmen Peláez-Moreno', 'Anselmo Peñas']
I am writing a research paper on a idea On Concept Lattices as Information Channels
[{"from": "human", "value": "I am writing a research paper on a idea On Concept Lattices as Information Channels"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  explores the idea that a concept lattice is an in- formation channel beten objects and attributes. For this purpose  study the behaviour of incidences in L-formal contexts where L is the range of an information-theoretic entropy function. Examples of such data abound in machine learning and data mining, e.g. confusion matri- ces of multi-class classifiers or document-term matrices.  use a ll- motivated information-theoretic heuristic, the maximization of mutual information, that in our conclusions provides a flavour of feature selection providing and information-theory explanation of an established practice in Data Mining, Natural Language Processing and Information Retrieval applications, viz. stop-wording and frequency thresholding.  also in- troduce a post-clustering class identification in the presence of confusions and a flavour of term selection for a multi-label document classification task."}]