## 4 Experiments (Application to [PERSON]'s Airline Passenger Satisfaction Data) and Discussion
Then, a single solution characterized, first, by the highest test-data accuracy and, second, by the highest interpretability is selected from that front approximation. The results from all partial experiments are averaged. The experiment is then repeated 10 times for different initializations of our approach. The averaged results are shown in Table 10. The only available alternative approach is the aforementioned decision tree of [24] with 7:3 learning-test data split ratio. Figure 6 summarizes, in an analogous form as in Figure 5, details of our approach (the left part of a block scheme of Figure 6) and the alternative method [24] (the right part of that block scheme) for the purpose of their comparative analysis in terms of the cross-validation-based test-data accuracy and interpretability. The following stages are distinguished in performing that task: data preprocessing, data partitioning, preparation of single \(k\)-th experiment for 10-fold cross validation (exclusively for our approach), learning process, and calculation of final results. The method of [24] is outperformed by our approach both in terms of system's accuracy and interpretability. Our SPEA3-based approach slightly outperforms its SPEA2-based counterpart in terms of accuracy whereas both of them are characterized by comparable interpretability. Concluding the experimental section of our work, it is worth emphasizing that measurement of airline passenger satisfaction is a key factor for improving service quality in airline companies [9]. In turn, passenger-satisfaction-based service quality is a strategic tool for gaining competitive advantage [15]. Various specialized companies and agencies such as, e.g., J.D.POWER (see its last report [28]) or American Customer Satisfaction Index (see, e.g., its last ACSI Travel Report [29]) carry out, process, and analyze airline passenger satisfaction surveys to target performance activities that--by attracting more passengers--have a direct impact on profits and reputation. As far as attributes affecting airline passenger satisfaction are concerned, \"In-Flight Wi-Fi Service\" and \"Simplicity of Online Booking\" (an analogical attribute to \"Ease of Online Booking\" in our research) have been identified in [30] as those which should be optimized by airlines. According to [31], \"F&B\" (i.e., catering service) and \"In-flight entertainment\" are principal attributes that affect passenger satisfaction. In turn, four attributes, i.e., \"Ease of online booking\", \"e-ticketing\", \"Boarding\" (analogical attributes to \"Online boarding\" in our research), and \"Clearance time\" have been selected in [32] as significant and important factors considered by airline passengers. In contrast to different methods formulating various sets of attributes affecting airline passenger satisfaction, our approach--employing the modern fuzzy-genetic business-intelligence approach--discovers from the huge set of representative data not only a collection of most important attributes but also formulates a hierarchy of their significance. Moreover, our approach precisely (in percentage values shown in Tables 6-9) determines the level of significance of particular attributes from the airline passenger satisfaction perspective. In order to discover the real significance hierarchy of input attributes, the analysis of the effect of possible \"overlapping\" of some input attributes over the other ones is also performed. Even more, our approach generates collections of linguistic, fuzzy rules (shown in Tables 4 and 5) that provide a precise and easily-interpretable insight into the mechanisms \"connecting\" the selected input attributes with airline passenger satisfaction or dissatisfaction. For instance, our approach is able to discover in the airline data not only rather obvious mechanisms (see, e.g., rule No. 5 in solution No. 5: IF Seat comfort is high AND Flight distance is long THEN Passenger is satisfied) but also less obvious ones (see, e.g., rule No. 10 in solution No.