# Enhanced Feature Selection via Hierarchical Concept Modeling
## 1 Introduction
FCA's structured approach reduces noise and redundancy more effectively than flat methods, as it respects the hierarchical dependencies among features. Meanwhile, the DT model is well suited for hierarchical feature selection due to its top-down approach, which prioritizes attributes based on their classification power at each level. By constructing a hierarchy where critical features emerge at the root levels, DT minimizes the feature space efficiently. This targeted selection enables better classification performance with fewer attributes, particularly in cases where some features are only relevant at certain levels within the dataset hierarchy. For these reasons, hierarchical models can better manage complex data with interrelated features. FCA's logical structure and DT's decision nodes allow these models to adapt to varying feature relevance across levels, providing more accurate classification outcomes than flat selection methods. This adaptability is particularly beneficial in tasks where features exhibit varying importance depending on context or subclass. FCA [15; 16; 17; 18], invented by [PERSON] in 1982, is a method for data analysis based on concept lattice. It is widely used in information science to describe attributes and objects of information that can be represented in a hierarchical structure. FCA provides the relationships for generalization and specialization among concepts in the concept lattice. This method is rarely applied to feature selection in classification or other tasks. This study applied the advantage of general knowledge through calculation and selection of a root node in the hierarchical structure. The alternative approach of decision trees uses these powerful and popular tools for classification and prediction. Such classifier has a tree structure, where each node is either a leaf node or a branching decision node [11; 19]. Thisapproach is widely used to perform feature selection. The advantages of decision trees are intuitive appeal for knowledge expression, simple implementation, and high classification accuracy. Thus, this work applied both FCA and decision trees to reduce the number of features that need to be collected and also to provide better classification accuracy. Due to the different patterns of selecting general knowledge in both FCA and decision trees in hierarchical concept structure, FCA will be constructed a concept lattice structure based on logic and set theory while decision trees will be built a tree structure based on information gain. Moreover, the decision tree in this study is considered to provide a mathematical selection of the features with the optimization level based on paired-samples \(t\)-test. Next, the proposed models were tested and evaluated by using three popular algorithms for classification--namely, decision tree, support vector machine (SVM), and artificial neural network (ANN)--to assess the predictive performances with the presented approach using data from the UCI repository. This article is organized as follows. Section 2 briefly describes the machine learning techniques applied in this work. Section 3 presents the research methodology, followed by the experimental results and discussion in Section 4. Finally, Section 5 concludes the article.