Semantic decision Trees: A new learning system for the ID3-Based algorithm using a knowledge base

Abstract

The decision tree (DT) is a well-established tool used to classify information. Its efficiency in accessing data and the accuracy of its outcomes is largely dependent on the characteristics of the input data and the efficacy of the construction processes and algorithms. In this study, we are introducing an innovative approach called the Semantic Decision Tree (SDT). The SDT builds upon the concept of the Iterative Dichotomiser 3 (ID3) algorithm. Our proposed SDT's first application involves utilizing a knowledge base, intentionally designed to support the DT construction process. The primary goal here is to effectively address the multi-value bias selection issue, a recurring problem found in a number of DT algorithms. Building on this, we adopt an ontology summarization technique to fine-tune the splitting condition of the DT. This adjustment is influenced by the attribute's importance value, extracted directly from the ontology. This process leads to the creation of nodes that align more precisely with the dataset than those generated by traditional DT methods. To validate the robustness and precision of our proposed SDT, we evaluated it across four distinct datasets. In addition, we benchmarked the classification accuracy of our SDT against several leading baseline algorithms; the traditional ID3, C4.5, CART, and the Mutual Information Decision Tree (MIDT). The findings demonstrate that our proposed SDT outperforms these industry-standard algorithms in terms of accuracy. Further in-depth qualitative analysis showed that the modifications in information gain resulted in DT structures that align more naturally with human decision-making logic.