## 1 Introduction  
Business intelligence (BI), in general, aims at providing decision support--based on empirical information--for various business activities in different domains such as industry, science, technology, healthcare, commerce, defense, etc. [1, 2]. BI is often used as an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies [3] summarizing a huge set of models and analytical methods such as reporting, data warehousing, and data mining [1]. Data about the considered business are the core elements of BI. Modern data mining (or knowledge discovery in data) tools rooted in the field of computational intelligence have given rise to knowledge-based decision support systems (equipped with knowledge bases and inference engines [4]) which can significantly enhance the formal apparatus of BI. Knowledge-based BI approaches are well suited for decision support of business activities in aviation industry. It is due to the availability of huge amounts of data collected over time by airlines and airports on various aspects of their operation. A significant amount of domain knowledge is buried and hidden in such data. Effective knowledge-discovery approaches can reveal, in an automatic way, understandable and useful structures, trends and patterns in the considered data to improve accuracy and provide decision explanation in -aviation industry decision support. Many data mining techniques--including those rooted in the field of computational intelligence--have been applied to the airline data analysis--see, e.g., [5; 6; 7; 8; 9; 10]. However, their essential drawback is their non-transparent, black-box, and accuracy-oriented nature, i.e., they do not provide any deeper (or any) explanations and justifications of the decisions made. This work is an attempt to address this problem by providing a solution characterized both by high interpretability and transparency as well as by high accuracy in the airline passenger satisfaction study. The main goal and contribution of this paper is the application of our knowledge-discovery technique (fuzzy rule-based classification systems) characterized by genetically optimized interpretability-accuracy trade-off (see, e.g., [11; 12; 13; 14] for details) to decision support related to airline passenger satisfaction problems. The quality of service and passenger satisfaction in the airline industry are increasingly recognized as critical factors of business performance and strategic tools for gaining competitive advantage [15]. In particular, we discover the hierarchy of influence of particular input attributes upon the airline passenger satisfaction. We also analyze the effect of possible \"overlapping\" of some input attributes over the other ones from the passenger satisfaction point of view. In our approach, measures of system's interpretability and accuracy are the separate performance indices and optimization objectives in designing fuzzy rule-based classifiers (FRBCs) from data. Due to the complementary/contradictory nature of both optimization objectives, multi-objective evolutionary optimization algorithms (MOEOAs) are employed in the process of the FRBC's structure and parameter optimization which is also equivalent to the FRBC's interpretability-accuracy trade-off optimization (see, e.g., [16] for review of related work and [17; 18] for the single-objective optimization case). First, the recently published Kaggle's airline passenger satisfaction data set containing as many as 259,760 records and used in our experiments is briefly characterized. Then, main components of our FRBCs and their MOEOA-based learning and optimization are outlined. For comparison purposes, two MOEOAs are used: our generalization (referred to as SPEA3 [19; 20; 21]) of the well-known Strength Pareto Evolutionary Algorithm 2 (SPEA2) [22] and SPEA2 itself.