Paper Info
Title
Mixing independent classifiers
Abstract
In this study we deal with the mixing problem, which con- cerns combining the prediction of independently trained lo- cal models to form a global prediction. We deal with it from the perspective of Learning Classifier Systems where a set of classifiers provide the local models. Firstly, we formalise the mixing problem and provide both analytical and heuris- tic approaches to solving it. The analytical approaches are shown to not scale well with the number of local models, but are nevertheless compared to heuristic models in a set of function approximation tasks. These experiments show that we can design heuristics that exceed the performance of the current state-of-the-art Learning Classifier System XCS, and are competitive when compared to analytical solutions. Additionally, we provide an upper bound on the prediction errors for the heuristic mixing approaches. Categories and Subject Descriptors: G.1.2 Numerical Analysis: Approximation — Least squares approximation
Year
DOI
Venue
2007
10.1145/1276958.1277278
Genetic and Evolutionary Computation Conference
Keywords
Field
DocType
analytical solution,learning classifier systems,local model,heuristic approach,general terms: performance,theory. keywords: learning classifier systems,prediction error,current state-of-the-art learning classifier,analytical approach,global prediction,system xcs,information fu- sion.,algorithms,function approximation task,independent classifier,upper bound,numerical analysis,learning classifier system,function approximation,analytic solution,least squares approximation
Mathematical optimization,Heuristic,Function approximation,Computer science,Upper and lower bounds,Heuristics,Artificial intelligence,Classifier (linguistics),Information fusion,Machine learning,Learning classifier system
Conference
Citations 
PageRank 
References 
6
0.55
7
Authors
2
Name
Order
Citations
PageRank
Jan Drugowitsch1393.80
Alwyn M. Barry2302.20