Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Closed sets have been proven successful in the context of compacted data representation for association rule learning. Hover, their use is mainly descriptive, dealing only with unlabeled data.  shows that when considering labeled data, closed sets can be adapted for classification and discrimination purposes by conveniently contrasting covering properties on positive and negative examples.  formally prove that these sets characterize the space of relevant combinations of features for discriminating the target class. In practice, identifying relevant/irrelevant combinations of features through closed sets is useful in many applications: to compact emerging patterns of typical descriptive mining applications, to reduce the number of essential rules in classification, and to efficiently learn subgroup descriptions, as demonstrated in real-life subgroup discovery experiments on a high dimensional microarray data set.
['Gemma C. Garriga', 'Petra Kralj', 'Nada Lavrač']
I am writing a research paper on a idea Closed Sets for Labeled Data
[{"from": "human", "value": "I am writing a research paper on a idea Closed Sets for 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: Closed sets have been proven successful in the context of compacted data representation for association rule learning. Hover, their use is mainly descriptive, dealing only with unlabeled data.  shows that when considering labeled data, closed sets can be adapted for classification and discrimination purposes by conveniently contrasting covering properties on positive and negative examples.  formally prove that these sets characterize the space of relevant combinations of features for discriminating the target class. In practice, identifying relevant/irrelevant combinations of features through closed sets is useful in many applications: to compact emerging patterns of typical descriptive mining applications, to reduce the number of essential rules in classification, and to efficiently learn subgroup descriptions, as demonstrated in real-life subgroup discovery experiments on a high dimensional microarray data set."}]