Online rule fusion model based on formal concept analysis

A rule is an effective representation of knowledge in formal concept analysis (FCA), which can express the relations between concepts. One of the main research directions of FCA is to develop rule-based classification algorithms. Rule-based algorithms in FCA lack effective methods for analyze their generalization capability, which can provide an effective learning guarantee for the algorithm. To solve this problem and effectively improve the classification performance of rule-based algorithms in terms of speed and accuracy, this paper combines formal concept analysis with online learning theory to design an online rule fusion model based on FCA, named ORFM. First, the weak granular decision rule is proposed based on rule confidence. Second, the purpose of each iteration is to reduce the difference between the prediction rules extracted from the ORFM and the weak granular decision rules as much as possible so that the classifier model can be adjusted to the direction of the minimum regret growth rate, and the regret growth rate is 0 under the ideal state at the end of iteration. Third, it is proven that the regret of ORFM has an upper bound; that is, in an ideal state, the regret growth rate decreases rapidly with the increase in the number of iterations, eventually making the regret of the model no longer grow. This provides an effective learning guarantee for ORFM. Finally, experimental results on 16 datasets show that ORFM has better classification performance than other classifier models.