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Volume 1 (2) 2007

Title:Meta-Knowledge as an engine in Classifier Combination
Authors: Fredrick Edward Kitoogo, Venansius Baryamureeba
Published:©IJCIR Vol1 (2) 2007, PP. 74-86
Language: English


Abstract:
The use of classifier combination has taken center stage in machine learning research, where outputs of different classifiers are combined in various ways to achieve a perceived better performance than that of any of the base classifiers involved in the combination. Many a research has however not empirically justified the use of the participating classifiers in a combination. This work looks at the usage of meta-knowledge that expresses the performance of each learning method on diverse domains to choose the most suitable learning algorithms suited for a combination for particular domains. The meta-knowledge is considered in this work is limited to the characteristics of the data involved. The approach works by having a learning algorithm that learns and describes how the data characteristics and the combined learning algorithms relate. Given a new learning domain, the domain characteristics are measured, and from the induced relationship, a selection of the most suitable base algorithms for combination will be done. The results of this work provide a fundamental step in achieving a cohesive framework for classifier combination. View full Article

General Terms: Computer Science, Language Processing
Additional Key Words and Phrases: classifier combination, clustering, machine learning, meta-knowledge
Categories and Subject Descriptors: I.5.2 [Pattern Recognition]: Design Methodology—Classifier design and evaluation; I.5.3 [Pattern Recognition]: Clustering—Algorithms; I.2.7 [Artificial Intelligence]: Natural Language Processing—language parsing and understanding