Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Multirelational classification aims at discovering useful patterns across multiple inter-connected tables (relations) in a relational database. Many traditional learning techniques, hover, assume a single table or a flat file as input (the so-called propositional algorithms). Existing multirelational classification approaches either “upgrade” mature propositional learning methods to deal with relational presentation or extensively “flatten” multiple tables into a single flat file, which is then solved by propositional algorithms. This article reports a multiple view strategy—where neither “upgrading” nor “flattening” is required—for mining in relational databases. Our approach learns from multiple views (feature set) of a relational databases, and then integrates the information acquired by individual view learners to construct a final model. Our empirical studies show that the method compares ll in comparison with the classifiers induced by the majority of multirelational mining systems, in terms of accuracy obtained and running time needed. The paper explores the implications of this finding for multirelational research and applications. In addition, the method has practical significance: it is appropriate for directly mining many real-world databases.
['Hongyu Guo', 'Herna L. Viktor']
I am writing a research paper on a idea Multirelational classification: a multiple view approach
[{"from": "human", "value": "I am writing a research paper on a idea Multirelational classification: a multiple view approach"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Multirelational classification aims at discovering useful patterns across multiple inter-connected tables (relations) in a relational database. Many traditional learning techniques, hover, assume a single table or a flat file as input (the so-called propositional algorithms). Existing multirelational classification approaches either \u201cupgrade\u201d mature propositional learning methods to deal with relational presentation or extensively \u201cflatten\u201d multiple tables into a single flat file, which is then solved by propositional algorithms. This article reports a multiple view strategy\u2014where neither \u201cupgrading\u201d nor \u201cflattening\u201d is required\u2014for mining in relational databases. Our approach learns from multiple views (feature set) of a relational databases, and then integrates the information acquired by individual view learners to construct a final model. Our empirical studies show that the method compares ll in comparison with the classifiers induced by the majority of multirelational mining systems, in terms of accuracy obtained and running time needed. The paper explores the implications of this finding for multirelational research and applications. In addition, the method has practical significance: it is appropriate for directly mining many real-world databases."}]