Abstract
The main objective and contribution of this paper is the application of our knowledge-discovery business-intelligence technique (fuzzy rule-based classification systems) characterized by genetically optimized interpretability-accuracy trade-off (using multi-objective evolutionary optimization algorithms) to decision support related to airline passenger satisfaction problems. Recently published and accessible at Kaggle’s repository airline passengers satisfaction data set containing 259,760 records is used in our experiments. A comparison of our approach with an alternative method (using SAS-system’s accuracy-oriented prediction tools to determine the attribute importance hierarchy) is also performed showing the advantages of our method in terms of: (i) discovering the actual hierarchy of attribute significance for passenger satisfaction and (ii) knowledge-discovery system’s interpretability-accuracy trade-off optimization. The main results and findings of our work include: (i) an introduction of the modern fuzzy-genetic business-intelligence solution characterized both by high interpretability and high accuracy to the airline passenger satisfaction decision support, (ii) an analysis of the effect of possible "overlapping" of some input attributes over the other ones in order to discover the real hierarchy of influence of particular input attributes upon the airline passengers satisfaction, and (iii) an extended cross-validation experiment confirming high effectiveness of our approach for different learning-test splits of the data set considered.
Highlights
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]
Modern data mining tools rooted in the field of computational intelligence have given rise to knowledge-based decision support systems which can significantly enhance the formal apparatus of BI
The main goal and contribution of this paper is the application of our knowledgediscovery technique characterized by genetically optimized interpretability-accuracy trade-off to decision support related to airline passenger satisfaction problems
Summary
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]. Knowledge-based BI approaches are well suited for decision support of business activities in aviation industry. 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. 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 knowledgediscovery 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 afore outlined main goal of the paper, i.e., the application of our approach to the Kaggle’s airline passenger satisfaction data and a comparative analysis with an alternative approach are presented and discussed
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