Can FCA-based Recommender System Suggest a Proper Classifier?

TL;DR

This paper presents a novel FCA-based recommendation algorithm that suggests the most suitable classifier for each object, aiming to improve classification accuracy by leveraging neighbor-based predictions.

Contribution

It introduces a new FCA-based algorithm for recommending classifiers to objects, enhancing classification accuracy through neighbor analysis.

Findings

Algorithm improves classification accuracy in experiments

Effective neighbor-based classifier recommendation demonstrated

Initial results show promise on real-world datasets

Abstract

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.