Can FCA-based Recommender System Suggest a Proper Classifier?
This addresses the challenge of selecting optimal classifiers in multiple classifier systems, but it appears incremental as it builds on existing neighbor-based and FCA methods.
The paper tackles the problem of improving classification accuracy by recommending a suitable classifier for each object based on its neighbors' correct classifications, using Formal Concept Analysis. The result is a new algorithm tested with initial experiments on real-world datasets, though no concrete numbers are provided.
The paper briefly introduces multiple classifier systems and describes a new algorithm, which improves classification accuracy by means of recommendation of a proper algorithm to an object classification. This recommendation is done assuming that a classifier is likely to predict the label of the object correctly if it has correctly classified its neighbors. The process of assigning a classifier to each object is based on Formal Concept Analysis. We explain the idea of the algorithm with a toy example and describe our first experiments with real-world datasets.