## 2 Kaggle's Airline Passenger Satisfaction Data  
As earlier mentioned, the recently published airline passenger satisfaction data set containing 259,760 records will be used in our experiments. It is a combination of two Excel data sets--referred to as \"satisfaction.xlsx\" and \"satisfaction_2015.xlsx\"--accessible at Kaggle's repository [23]; see also [24]. Each of them contains 129,880 records. Each record in both sets is characterized by 24 attributes. However, the attribute \"Online support\" occurring in the first set does not occur in the second set and the attribute \"Inflight service\" occurring in the second set does not occur in the first set. Following [24], both attributes were removed from the corresponding sets. Thus, each record of the final data set--obtained by merging the first and the second sets and referred to as the airline passenger satisfaction data set--is characterized by 23 attributes. Table 1 presents details of particular records of the final data set used in our experiments. The first attribute (i.e., \"id\" of a passenger--unique for each record) will not be used in our experiments. The second attribute, i.e., \"satisfaction_v2\" is the class attribute in our experiments. For better clarity, from now on its name will be replaced by the phrase \"Passenger is\" followed by the class label, i.e., either \"neutral or dissatisfied\" or \"satisfied\". The remaining 21 attributes are the input attributes. They include four numerical attributes, 13 qualitative ordinal attributes, and four qualitative nominal (sometimes referred to as categorical) attributes. Such data will be used in our experiments presented later in the paper. It is worth emphasizing that the data are almost perfectly balanced. As shown in Table 1, the classes \"neutral or dissatisfied\" and \"satisfied\" are represented by 49% and 51% of all data records, respectively. Therefore, there is no bias when accuracy is used as one of performance measures of the obtained FRBCs. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**No. Attribute Name** & **Attribute Type** & \multicolumn{3}{c}{**Attribute Domain Details (\%—Percentage of the Overall Number of Samples)**} \\ \hline
1. & id & numerical & Passenger’s id (not used in our experiments) & Passenger’s id (not used in our experiments) & Passenger’s id (not used in our experiments) \\
2. & satisfaction\_v2 *1 & class label & 2 class labels: ”neutral or dissatisfied” (49\%) and ”satisfied” (51\%) & 2 class labels: ”female” (51\%) and ”male” (49\%) & \\
3. & Gender & nominal *2 & 2 terms: “legal customer” (82\%) and “disloyal customer” (18\%) & 2 terms: “legal customer” (82\%) and “disloyal customer” (18\%) & 2 terms: “legal customer” (82\%) and “disloyal customer” (18\%) & 2 terms: “legal customer” (39.2, std. deviation: 15.1) & 2 terms: “personal travel” (31\%) and “business travel” (69\%) & 2 terms: “eco” (46\%), “business” (48\%), and “eco plus” (7\%) & 2 terms: “eco” (48\%), “business” (48\%), and “eco plus” (7\%) & 2 terms: “legal customer” (48\%), “business travel” (48\%) & 2 terms: “legal customer” (48\%)  
Methodology: An Outline of Main Components of the Proposed FRBCs and Their MOEOA-Based Learning and Optimization  
For convenience of the reader, in this section we briefly outline main components of our approach--see [11; 12; 13; 19] for details and discussion. In general, we consider FRBC with \(n\) input attributes \(x_{1},x_{2},\ldots,x_{n}\) (including both numerical and qualitative ones) and an output--a fuzzy set over the set \(Y=\{y_{1},y_{2},\ldots,y_{c}\}\) of \(c\) class labels.