scholarly journals Business Intelligence in Airline Passenger Satisfaction Study—A Fuzzy-Genetic Approach with Optimized Interpretability-Accuracy Trade-Off

2021 ◽  
Vol 11 (11) ◽  
pp. 5098
Author(s):  
Marian B. Gorzałczany ◽  
Filip Rudziński ◽  
Jakub Piekoszewski
Keyword(s):  
Decision Support ◽  
Knowledge Discovery ◽  
Business Intelligence ◽  
Classification Systems ◽  
Data Set ◽  
Trade Off ◽  
Passenger Satisfaction ◽  
Airline Passengers ◽  
Airline Passenger ◽  
Fuzzy Genetic

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.

scholarly journals Decision support system for promotion of Faculty of Business and Economics Mendel University

2012 ◽  
Vol 60 (2) ◽  
pp. 443-448
Author(s):  
Pavel Turčínek ◽  
Arnošt Motyčka
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Secondary School ◽  
Knowledge Discovery ◽  
Business Intelligence ◽  
Knowledge Discovery In Databases ◽  
Optimum Number ◽  
Product Knowledge ◽  
Study Programs ◽  
Decision Making Processes

Decreasing number of secondary school graduates means that, for college, it becomes more difficult to fulfill guide number of newly admitted students. In order to maintain an optimum number of registered students, the Faculty of Business and Economics decided to support activities which increase the interest of its accredited programs.Potential students should be treated as customers to whom we want to offer a product – knowledge, skills and competencies. Promoting study programs PEF MENDELU is handled by PR department in collaboration with several students.Availability of resources for promotion is limited. It is crucial to deciding how to deal with these sources. By creating a system for monitoring and decision support, we provide all interested collaborators tool to improve decision-making processes.The system itself will be built on the tools of Business Intelligence (BI) that can observe consumer trends, identify customer segments and other important information. The BI emphasizes the use of OLAP technology for data processing. In the collected data about students is hidden a large amount of information that can be obtained using techniques such as knowledge discovery in databases.This article aims to describe the methodology for solving problems and show the application, which result in support of decision-making processes in the propagation PEF MENDELU, which should also lead to the efficiency of spending on this activity.

A data mining approach for lubricant-based fault diagnosis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
James Wakiru ◽  
Liliane Pintelon ◽  
Peter Muchiri ◽  
Peter Chemweno
Keyword(s):  
Decision Support ◽  
Fault Diagnosis ◽  
Knowledge Discovery ◽  
Predictive Accuracy ◽  
Principal Component ◽  
Gradient Boosting ◽  
Data Set ◽  
Content Type ◽  
Embedded Knowledge ◽  
Extreme Gradient Boosting

PurposeThe purpose of this paper is to develop a maintenance decision support system (DSS) framework using in-service lubricant data for fault diagnosis. The DSS reveals embedded patterns in the data (knowledge discovery) and automatically quantifies the influence of lubricant parameters on the unhealthy state of the machine using alternative classifiers. The classifiers are compared for robustness from which decision-makers select an appropriate classifier given a specific lubricant data set.Design/methodology/approachThe DSS embeds a framework integrating cluster and principal component analysis, for feature extraction, and eight classifiers among them extreme gradient boosting (XGB), random forest (RF), decision trees (DT) and logistic regression (LR). A qualitative and quantitative criterion is developed in conjunction with practitioners for comparing the classifier models.FindingsThe results show the importance of embedded knowledge, explored via a knowledge discovery approach. Moreover, the efficacy of the embedded knowledge on maintenance DSS is emphasized. Importantly, the proposed framework is demonstrated as plausible for decision support due to its high accuracy and consideration of practitioners needs.Practical implicationsThe proposed framework will potentially assist maintenance managers in accurately exploiting lubricant data for maintenance DSS, while offering insights with reduced time and errors.Originality/valueAdvances in lubricant-based intelligent approach for fault diagnosis is seldom utilized in practice, however, may be incorporated in the information management systems offering high predictive accuracy. The classification models' comparison approach, will inevitably assist the industry in selecting amongst divergent models' for DSS.

Evolutionary Intelligent Data Warehousing Approach to Knowledge Discovery Systems: Dynamic Cubing

2019 ◽  
Vol 13 ◽  
Author(s):  
Harkiran Kaur ◽  
Kawaljeet Singh ◽  
Tejinder Kaur
Keyword(s):  
Knowledge Discovery ◽  
Data Warehouse ◽  
Business Intelligence ◽  
End Users ◽  
Processing Stage ◽  
Information Update ◽  
On Line ◽  
Analytical Processing ◽  
Warehouse Operations ◽  
Made In

Background: Numerous E – Migrants databases assist the migrants to locate their peers in various countries; hence contributing largely in communication of migrants, staying overseas. Presently, these traditional E – Migrants databases face the issues of non – scalability, difficult search mechanisms and burdensome information update routines. Furthermore, analysis of migrants’ profiles in these databases has remained unhandled till date and hence do not generate any knowledge. Objective: To design and develop an efficient and multidimensional knowledge discovery framework for E - Migrants databases. Method: In the proposed technique, results of complex calculations related to most probable On-Line Analytical Processing operations required by end users, are stored in the form of Decision Trees, at the pre- processing stage of data analysis. While browsing the Cube, these pre-computed results are called; thus offering Dynamic Cubing feature to end users at runtime. This data-tuning step reduces the query processing time and increases efficiency of required data warehouse operations. Results: Experiments conducted with Data Warehouse of around 1000 migrants’ profiles confirm the knowledge discovery power of this proposal. Using the proposed methodology, authors have designed a framework efficient enough to incorporate the amendments made in the E – Migrants Data Warehouse systems on regular intervals, which was totally missing in the traditional E – Migrants databases. Conclusion: The proposed methodology facilitate migrants to generate dynamic knowledge and visualize it in the form of dynamic cubes. Applying Business Intelligence mechanisms, blending it with tuned OLAP operations, the authors have managed to transform traditional datasets into intelligent migrants Data Warehouse.

Perbandingan Optimasi Feature Selection pada Naïve Bayes untuk Klasifikasi Kepuasan Airline Passenger

2021 ◽  
Vol 5 (3) ◽  
pp. 527-533
Author(s):  
Yoga Religia ◽  
Amali Amali
Keyword(s):  
Feature Selection ◽  
Customer Satisfaction ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Point Of View ◽  
Classification Model ◽  
Passenger Satisfaction ◽  
Airline Passenger ◽  
Bayes Algorithm

The quality of an airline's services cannot be measured from the company's point of view, but must be seen from the point of view of customer satisfaction. Data mining techniques make it possible to predict airline customer satisfaction with a classification model. The Naïve Bayes algorithm has demonstrated outstanding classification accuracy, but currently independent assumptions are rarely discussed. Some literature suggests the use of attribute weighting to reduce independent assumptions, which can be done using particle swarm optimization (PSO) and genetic algorithm (GA) through feature selection. This study conducted a comparison of PSO and GA optimization on Naïve Bayes for the classification of Airline Passenger Satisfaction data taken from www.kaggle.com. After testing, the best performance is obtained from the model formed, namely the classification of Airline Passenger Satisfaction data using the Naïve Bayes algorithm with PSO optimization, where the accuracy value is 86.13%, the precision value is 87.90%, the recall value is 87.29%, and the value is AUC of 0.923.

scholarly journals Robust estimation of vertical symmetry axis models via joint migration inversion: Including multiples in anisotropic parameter estimation

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. C57-C74 ◽  
Author(s):  
Abdulrahman A. Alshuhail ◽  
Dirk J. Verschuur
Keyword(s):  
Symmetry Axis ◽  
Transverse Isotropy ◽  
Half Cycle ◽  
Anisotropic Parameter ◽  
Data Set ◽  
Trade Off ◽  
Trade Offs ◽  
Highly Nonlinear ◽  
Wide Range ◽  
Internal Multiples

Because the earth is predominately anisotropic, the anisotropy of the medium needs to be included in seismic imaging to avoid mispositioning of reflectors and unfocused images. Deriving accurate anisotropic velocities from the seismic reflection measurements is a highly nonlinear and ambiguous process. To mitigate the nonlinearity and trade-offs between parameters, we have included anisotropy in the so-called joint migration inversion (JMI) method, in which we limit ourselves to the case of transverse isotropy with a vertical symmetry axis. The JMI method is based on strictly separating the scattering effects in the data from the propagation effects. The scattering information is encoded in the reflectivity operators, whereas the phase information is encoded in the propagation operators. This strict separation enables the method to be more robust, in that it can appropriately handle a wide range of starting models, even when the differences in traveltimes are more than a half cycle away. The method also uses internal multiples in estimating reflectivities and anisotropic velocities. Including internal multiples in inversion not only reduces the crosstalk in the final image, but it can also reduce the trade-off between the anisotropic parameters because internal multiples usually have more of an imprint of the subsurface parameters compared with primaries. The inverse problem is parameterized in terms of a reflectivity, vertical velocity, horizontal velocity, and a fixed [Formula: see text] value. The method is demonstrated on several synthetic models and a marine data set from the North Sea. Our results indicate that using JMI for anisotropic inversion makes the inversion robust in terms of using highly erroneous initial models. Moreover, internal multiples can contain valuable information on the subsurface parameters, which can help to reduce the trade-off between anisotropic parameters in inversion.

scholarly journals Üzleti intelligencia megoldások alkalmazásának sikertényezői - A hazai szolgáltató szektor nagyvállalatainak körében végzett mélyinterjús kutatás

2019 ◽  
Vol 19 (2) ◽  
pp. 23
Author(s):  
Gergely Görcsi ◽  
Gergő Barta ◽  
Zsuzsanna Széles
Keyword(s):  
Decision Support ◽  
Continuous Improvement ◽  
Business Intelligence ◽  
Support Functions ◽  
Term Operation ◽  
Success Criteria ◽  
The World ◽  
Depth Interviews ◽  
Insight Into

A vállalatok működése szempontjából a döntéstámogató funkció folyamatos fejlesztése, monitorozása kiemelt jelentőségű, hiszen az vezetést támogató eszközként segíti a menedzsmentfeladatok ellátását. Az üzleti intelligencia (business intelligence, BI) olyan infokommunikációs megoldás, mely a vállalati rendszerekből különböző adatforrásokat felhasználva képes az adatok összekapcsolására és elemzésére. A napi üzletmenet gördülékeny biztosítása céljából alkalmazott tranzakciós rendszerektől eltérően a BI-eszközök beszámolás orientáltak, a fókusz a döntéstámogatásra helyeződik. A kutatás a fogalmak tisztázását követően képet ad a legfrissebb üzleti intelligencia trendekről. A tanulmány szakmai mélyinterjúk elemzésén keresztül betekintést nyújt az üzleti intelligencia megoldások világába. A kutatás eredményeként az olvasó képet kaphat a BI bevezetésétől várt eredményekről, az implementáció és a hosszú távú működtetés sikerkritériumait illetően. --- Gergely GORCSI - Gergo BARTA - Zsuzsanna SZELES Success criteria for the application of business intelligence solutions In the running of any given company, continuous improvement and monitoring of decision support functions is crucial for such activities to serve as tools to support management tasks. Business Intelligence (BI) is an infocommunication tool that connects and analyses data from corporate systems using varied data sources. Unlike transactional systems that are used to ensure the sound operation of day-to-day business, BI tools are report-oriented, and focus on decision support. Reviewing related concepts, this research gives an overview of the latest business intelligence trends. Our study sets out to provide an insight into the world of business intelligence solutions by analysing professional, in-depth interviews. Through our research, one will become familiar with the results expected from the introduction of BI, in relation to the success criteria of its implementation and long-term operation.

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