Multi-Objective Evolutionary Rule-Based Classification with Categorical Data

Jiménez, Fernando; Martínez, Carlos; Miralles-Pechuán, Luis; Sciavicco, Gracia and Guido

doi:10.3390/e20090684

Cited by 7 publications

(6 citation statements)

References 48 publications

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“…However, the main problems are that the datasets are not available due to security concerns, and that the datasets are extremely unstable. Many machine learning‐based solutions have been proposed for credit card fraud detection 3,4,6,7,9‐12 …”

Section: Introductionmentioning

confidence: 99%

Predicting credit card fraud using multipurpose classification based on evolutionary rules

Dirik

2022

Security and Privacy

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Due to advances in Internet technology, credit card transactions are increasing faster than ever before. This has led to a fraud problem that affects businesses, cooperating institutions, and government agencies. Credit card fraud occurs when a third party uses your credit card or credit account for an unauthorized transaction. Given the increased security of credit card transactions, fraudsters are developing new tricks or exploiting new vulnerabilities. Thanks to evolving technology, it is possible to analyze the use of maliciously obtained data by studying the time and cost associated with account switching transactions. In this study, we propose the ENORA and NSGA-II methods to create rule-based classifiers that can be easily interpreted using the credit card fraud diagnosis dataset created by analyzing the time intervals of individuals' credit card usage. In this experiment, credit card payments are classified as fraudulent or nonfraudulent based on several variables. The experiments were conducted in full training mode and 10-fold cross-validation mode. The performance of the algorithm was measured by accuracy, area under the receiver operating characteristic (ROC) curve, mean square error, proportion of true positives, proportion of false positives, precision, recall, F-measure, area under the curve (AUC), andMatthews correlation coefficient. The classification performance of the model using the correct classification ratio (ACC) is as follows: NSGA-II = 94.244%, ENORA = 93.236%. The performance of the other metrics is also discussed in detail in the results section. These results illustrate the significant predictive power of the proposed credit card theft models. After a thorough statistical analysis of our results, we found that the proposed strategy is capable of producing accurate and easy-to-understand categorization models.

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Section: Introductionmentioning

confidence: 99%

Predicting credit card fraud using multipurpose classification based on evolutionary rules

Dirik

2022

Security and Privacy

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“…MOO has attracted increasing number of applications in chemical engineering. 2 Recent papers on MOO applications by active researchers in process systems engineering are design and control of intensified distillation sequence, 4 model simulation of ethylene oxide formation in a packed bed membrane reactor, 5 energy system design for a commercial building, 6 resilient design and operation of process systems, 7 generation of rule-based classifier for categorical data, 8 design of solid oxide fuel cell with a gas turbine hybrid system, 9 design of biomass supply chains, 10 interactive MOO of biochemical processes with parametric uncertainty, 11 design of carbonhydrogen-oxygen symbiosis networks, 12 sustainable water management, 13 and batch distillation optimization for economic and environmental objectives. 14 Our group has been active in MOO research for about 2 decades and has contributed two books 15,16 and many articles on MOO and its applications.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Weighting and Selection Methods for Pareto-Optimal Solutions of Multiobjective Optimization in Chemical Engineering Applications

Wang

Parhi

Rangaiah

et al. 2020

Ind. Eng. Chem. Res.

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Optimization in chemical engineering often involves two or more objectives, which are conflicting. Multiobjective optimization (MOO) generates a set of equally good solutions from the perspective of objectives used; these solutions are known as nondominated or Paretooptimal solutions. Although MOO has become popular in chemical engineering in the past 20 years, majority of studies are limited to finding Pareto-optimal solutions and only some papers discussed the selection of one nondominated solution for implementation. Thus, the main research gap in the application of MOO in chemical engineering is the very limited use of selection or ranking methods. To address this gap, 8 weighting methods and 4 more selection methods (besides 10 methods analyzed by Wang and Rangaiah) are carefully chosen and implemented in an MS Excel-based program. Then, all these methods are applied to 12 mathematical and 13 chemical engineering problems. The results of this extensive analysis indicate that the entropy method, criteria importance through intercriteria correlation method, and best− worst method are better for determining weights for objectives. Further, out of the four additional selection methods tested, multiattributive border approximation area comparison is suggested for ranking Pareto-optimal solutions.

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“…The supervised models are created from observations which consist of a set of input and output data. A supervised model describes the function which associates inputs with output [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Compressed kNN: K-Nearest Neighbors with Data Compression

Salvador-Meneses

Ruiz-Chavez

García-Rodríguez

2019

Entropy

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The kNN (k-nearest neighbors) classification algorithm is one of the most widely used non-parametric classification methods, however it is limited due to memory consumption related to the size of the dataset, which makes them impractical to apply to large volumes of data. Variations of this method have been proposed, such as condensed KNN which divides the training dataset into clusters to be classified, other variations reduce the input dataset in order to apply the algorithm. This paper presents a variation of the kNN algorithm, of the type structure less NN, to work with categorical data. Categorical data, due to their nature, can be compressed in order to decrease the memory requirements at the time of executing the classification. The method proposes a previous phase of compression of the data to then apply the algorithm on the compressed data. This allows us to maintain the whole dataset in memory which leads to a considerable reduction of the amount of memory required. Experiments and tests carried out on known datasets show the reduction in the volume of information stored in memory and maintain the accuracy of the classification. They also show a slight decrease in processing time because the information is decompressed in real time (on-the-fly) while the algorithm is running.

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Multi-Objective Evolutionary Rule-Based Classification with Categorical Data

Cited by 7 publications

References 48 publications

Predicting credit card fraud using multipurpose classification based on evolutionary rules

Predicting credit card fraud using multipurpose classification based on evolutionary rules

Analysis of Weighting and Selection Methods for Pareto-Optimal Solutions of Multiobjective Optimization in Chemical Engineering Applications

Compressed kNN: K-Nearest Neighbors with Data Compression

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