CHADE: Metalearning with Classifier Chains for Dynamic Combination of Classifiers

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Pinto, F (Author) Other; Carlos Soares (Author) FEUP; João Mendes-Moreira (Author) FEUP

Conference proceedings International

Title: Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part I

Pages: 410-425

Indexing

Scopus - 14 Citations

Other information

Authenticus ID: P-00K-TH4

DOI: 10.1007/978-3-319-46128-1_26

Abstract (EN): Dynamic selection or combination (DSC) methods allow to select one or more classifiers from an ensemble according to the characteristics of a given test instance x. Most methods proposed for this purpose are based on the nearest neighbours algorithm: it is assumed that if a classifier performed well on a set of instances similar to x, it will also perform well on x. We address the problem of dynamically combining a pool of classifiers by combining two approaches: metalearning and multi-label classification. Taking into account that diversity is a fundamental concept in ensemble learning and the interdependencies between the classifiers cannot be ignored, we solve the multi-label classification problem by using a widely known technique: Classifier Chains (CC). Additionally, we extend a typical metalearning approach by combining metafeatures characterizing the interdependencies between the classifiers with the base-level features.We executed experiments on 42 classification datasets and compared our method with several state-of-the-art DSC techniques, including another metalearning approach. Results show that our method allows an improvement over the other metalearning approach and is very competitive with the other four DSC methods. © Springer International Publishing AG 2016.

Language: English

Type (Professor's evaluation): Scientific