New adaptive intelligent grey wolf optimizer based multi-objective quantitative classification rules mining approaches

Abstract

The classification rule mining problem is one of the most important tasks of data mining. A constructed classification model is needed to have high accuracy, comprehensiveness, interestingness, etc. Furthermore, when the data composed of quantitative, numerical, or mixed data types, automatically discovering the appropriate intervals at the time of the mining process is a hard problem. The standard classification algorithms in the literature do not find the intervals of the quantitative attributes in the rules and this is performed a priori that causes the modification of datasets. Automatically constructing a successful classification model consisting of an explainable rule set without changing or modifying the data in artificial intelligence and machine learning is a very hot topic. Due to the philosophy of constantly researching to discover more accurate, surprising, and comprehensible rule sets and the absence of the most effective algorithm for all kinds of data sets, new methods or new versions of existing methods are proposed. In this study, automatic mining of high-quality rule set is handled as a multi-objective optimization problem due to the nature of the necessities and novel adaptive multi-objective intelligent search and optimization algorithms based on Grey Wolf Optimizer are proposed for this task. The datasets are considered as search spaces and the proposed adaptive Pareto based multi-objective Grey Wolf Optimizer algorithms are designed and applied as search methods for automatically discovering the high-quality rules. The proposed intelligent methods and successful classification algorithms such as naive Bayes, k-NN, support vector machines, decision trees, and RIPPER are tested in five real-world data sets with different characteristics. The obtained results show the efficiency of the proposed intelligent search and optimization methods.