Category Encoders: a scikit-learn-contrib package of transformers for encoding categorical data

McGinnis, William D; Siu, Chapman; Schult, Andre; Huang, Hanyu

doi:10.21105/joss.00501

Cited by 28 publications

(13 citation statements)

References 2 publications

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“…Some common categorical encoding methods are listed in Table 1 . For a more extensive list of encoding techniques, one can refer to the work of other researchers [ 89 , 90 , 91 ]. It has to be emphasised that the cardinality of variables has to be taken into account, since most of them are not suitable for high cardinality due to the so-called curse of dimensionality.…”

Section: ML Methods For Financial Fraud Data Classificationmentioning

confidence: 99%

Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications

Stojanović

Božić

Hofer-Schmitz

et al. 2021

Sensors

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Financial technology, or Fintech, represents an emerging industry on the global market. With online transactions on the rise, the use of IT for automation of financial services is of increasing importance. Fintech enables institutions to deliver services to customers worldwide on a 24/7 basis. Its services are often easy to access and enable customers to perform transactions in real-time. In fact, advantages such as these make Fintech increasingly popular among clients. However, since Fintech transactions are made up of information, ensuring security becomes a critical issue. Vulnerabilities in such systems leave them exposed to fraudulent acts, which cause severe damage to clients and providers alike. For this reason, techniques from the area of Machine Learning (ML) are applied to identify anomalies in Fintech applications. They target suspicious activity in financial datasets and generate models in order to anticipate future frauds. We contribute to this important issue and provide an evaluation on anomaly detection methods for this matter. Experiments were conducted on several fraudulent datasets from real-world and synthetic databases, respectively. The obtained results confirm that ML methods contribute to fraud detection with varying success. Therefore, we discuss the effectiveness of the individual methods with regard to the detection rate. In addition, we provide an analysis on the influence of selected features on their performance. Finally, we discuss the impact of the observed results for the security of Fintech applications in the future.

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Section: ML Methods For Financial Fraud Data Classificationmentioning

confidence: 99%

Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications

Stojanović

Božić

Hofer-Schmitz

et al. 2021

Sensors

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show abstract

“…The sum of the latent representations of their features represents each user, which enables us to generalize to new users and new source-destinations. For hybrid matrix factorization, we have a set of features encoding customer information using one-hot encoding [ 51 ] to denote the various customer features, such as gender, age range, nationality, and airline membership tier. The next set of features identifies the source-destination pair.…”

Section: Methodsmentioning

confidence: 99%

Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?

Thirumuruganathan

Jung

Robillos

et al. 2021

Electron Commer Res

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Using 27 million flight bookings for 2 years from a major international airline company, we built a Next Likely Destination model to ascertain customers’ next flight booking. The resulting model achieves an 89% predictive accuracy using historical data. A unique aspect of the model is the incorporation of self-competence, where the model defers when it cannot reasonably make a recommendation. We then compare the performance of the Next Likely Destination model in a real-life consumer study with 35,000 actual airline customers. In the user study, the model obtains a 51% predictive accuracy. What happened? The Individual Behavior Framework theory provides insights into possibly explaining this inconsistency in evaluation outcomes. Research results indicate that algorithmic approaches in competitive industries must account for shifting customer preferences, changes to the travel environment, and confounding business effects rather than relying solely on historical data.

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“…This research is mainly completed with the help of Scikit-leam (sklearn) toolkit based on Python language [17]. The toolkit contains a variety of commonly used machine learning methods, and regression, clustering and preprocessing modules.…”

Section: Construction Of Academic Performance Prediction Model Of Online Education Based On Rf Algorithmmentioning

confidence: 99%

Academic Performance Prediction Method of Online Education using Random Forest Algorithm and Artificial Intelligence Methods

2021

Int. J. Emerg. Technol. Learn.

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In order to improve the teaching quality of online education, the prediction method of students' online academic performance has been studied. First, the learning analysis, artificial intelligence (AI) and other related theoretical concepts are analyzed and introduced. Then, the decision tree of single classification algorithm and the random forest (RF) of ensemble learning algorithm are analyzed, and the academic performance prediction model of online education is constructed by RF algorithm. Finally, the data of education platform is used for empirical analysis to verify the reliability and practicability of the academic performance prediction algorithm of online education. The connotation of learning analysis, the role and elements of learning analysis in the learning process are introduced. The algorithm principle of RF and decision tree is analyzed. By using the idea of information entropy and discretization, the continuous variables are processed to improve the fitting degree of the algorithm. The model is evaluated by empirical analysis, and the test accuracy of several different algorithms is compared. It is found that the prediction accuracy of the RF algorithm is more than 90%, which shows that the prediction method can help teachers and students to carry out better teaching and learning activities, so as to better improve students' ability to master knowledge. It is hoped that the result can provide some reference for the management of students' learning behavior and the optimization of teachers' teaching strategies in online learning activities

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Category Encoders: a scikit-learn-contrib package of transformers for encoding categorical data

Cited by 28 publications

References 2 publications

Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications

Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications

Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?

Academic Performance Prediction Method of Online Education using Random Forest Algorithm and Artificial Intelligence Methods

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