title: Coupling K-nearest neighbors with logistic regression in case-based reasoning.
abstract: Case-based reasoning (CBR) systems use similarity functions to solve new problems with past situations. K-nearest neighbors algorithm (K-NN) have been used in CBR systems to define new cases status according to characteristics of past nearest cases. We proposed a new hybrid approach combining logistic regression (LR) with K-NN to optimize CBR classification. First, we analyzed the knowledge database by LR procedures and the Pearson residuals of the LR model were used to define cases' utility of the knowledge database into K-NN. Secondly, we compared the classification performances of LR model and K-NNs coupled or not with LR. Our results showed that the information provided by the residuals could be used to optimize the settings of K-NN and to improve CBR classification.
authors.affiliation: Faculté de médecine, Université rennes 1, Rennes Cedex 9, France. boriscampillo@gmail.com
authors.forename: Boris
authors.initials: B
authors.lastname: Campillo-Gimenez
authors.forename: Sahar
authors.initials: S
authors.lastname: Bayat
authors.forename: Marc
authors.initials: M
authors.lastname: Cuggia
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016022:Case-Control Studies; D003198:Computer Simulation; D016208:Databases, Factual; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D016015:Logistic Models; D010363:Pattern Recognition, Automated / Q000379:methods*; D012044:Regression Analysis