Feature Importance for Clustering

Nápoles, Gonzalo; Griffioen, Niels; Khoshrou, Samaneh; Güven, Çiçek

doi:10.1007/978-3-031-49018-7_3

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…Such a network can be created by randomly selecting a subset of actors. To ensure the uniformity of the samples, there are various methods, among which the most famous are [21,22], [23]: walkover in width; random walkover; reweighted random walkover; random walkover of Metropolis-Hastings.…”

Section: Fig 1 Classification Of Comments In Web Platformsmentioning

confidence: 99%

Information system for analyzing publicsentiment in web platforms based on machine learning

Uhryn,

Karachevtsev,

Tomka

et al. 2024

HAIT

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The systems for studying public sentiment in web platforms are analyzed. Various tools and methods for effectively determining the mood in textual data from web platforms are described, including the formalization of the social graph and the content graph.The process of classifying comments, which includes the systematization and categorization of statements, is investigated. Based on the studied dataset, information on customer reviews and hotel ratings in Europe from the booking.com web platform is selected. Taking into account the requirements of the information system and the results of the analysis, it is determined that in order to obtain better results in determining the emotional connotation of the texts of reviews and messages from users, the most appropriate is the use of machine learning methods, taking into account natural language methods for processing text data. When choosing a text vectorization method for machine learning, the Term Frequency Inverse Document Frequency Vectorizer was chosen as the most effective among the studied methods. The architectural structure of the studied system is proposed, which is aimed at effective interaction between components and modules. The LogisticRegression model is chosen to determine the publicmood. An information system has been developed that analyzes public sentiment about objects, uses advanced machine learning technologies to assess the emotional connotation of text comments, and provides users with insights and analysis of the results.

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Section: Fig 1 Classification Of Comments In Web Platformsmentioning

confidence: 99%

Information system for analyzing publicsentiment in web platforms based on machine learning

Uhryn,

Karachevtsev,

Tomka

et al. 2024

HAIT

View full text Add to dashboard Cite

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Imbalanced Data Challenges and Their Resolution to Improve Fraud Detection in Credit Card Transactions

Manda,

Kondapalli,

Malla

et al. 2024

Preprint

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This project addresses the critical issue of fraud detection in credit card transactions, an imperative conand cern for both financial institutions and cardholders. With the increasing sophistication of fraudulent activities, accurate identification and prevention of fraudulent transactions have become paramount. The study focuses on a dataset comprising credit card transactions conducted by European cardholders in September 2013. Notably, the dataset exhibits a severe class imbalance, with fraudulent transactions accounting for a mere 0.172% of the total. The primary objective of this research is to develop a robust machine-learning model capable of effectively discerning between legitimate and fraudulent transactions. The project commences with an extensive exploration of the dataset, encompassing checks for data imbalance, feature visualization, and analysis of feature interrelationships. Subsequently, four predictive models, including Random Forest, AdaBoost, Cat Boost, and XG Boost, were employed and evaluated. The dataset was partitioned into three subsets: a training set, a validation set, and a test set. Initial results showcased promising performance, with the Random Forest model yielding an Area Under the Curve (AUC) the core of 0.85 on the test set. The AdaBoost model achieved a slightly lower AUC score of 0.83, while the Cat Boost model, following 500 iterations, attained an AUC score of 0.86. The XG Boost model demonstrated exceptional promise, achieving a validation score of 0.984, and subsequently producing an AUC score of 0.974 on the test set. Further, the project introduced a Light GBM model, leveraging both train-validation split and cross-validation methods. The former yielded AUC scores of approximately 0.974 on the validation set and 0.946 on the test set. Cross-validation exhibited a similar effectiveness, culminating in an AUC score of 0.93 on the test predictions. This study not only underscores the efficacy of employing advanced machine learning techniques in fraud detection but also emphasizes the importance of model selection and evaluation in the context of imbalanced data. The findings provide valuable insights for financial institutions seeking to bolster their fraud detection capabilities, ultimately enhancing the security and trust of credit card transactions.

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Feature Importance for Clustering

Cited by 2 publications

References 13 publications

Information system for analyzing publicsentiment in web platforms based on machine learning

Information system for analyzing publicsentiment in web platforms based on machine learning

Imbalanced Data Challenges and Their Resolution to Improve Fraud Detection in Credit Card Transactions

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