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Articles 31 - 32 of 32
Full-Text Articles in Entire DC Network
Artificial Intelligence In The Fintech Sector – Legal And Regulatory Aspects, Marcin Rojszczak
Artificial Intelligence In The Fintech Sector – Legal And Regulatory Aspects, Marcin Rojszczak
internetowy Kwartalnik Antymonopolowy i Regulacyjny (internet Quarterly on Antitrust and Regulation)
The aim of the article is to discuss the legal consequences of implementing modern data processing techniques, in particular machine learning and Big Data analysis, in the fi nancial innovation sector (fi ntech). These techniques not only create new opportunities for data monetization for entities operating in the fi nancial sector, but also reveal new regulatory and supervisory challenges that need to be addressed.
Application Of Synthetic Informative Minority Over-Sampling (Simo) Algorithm Leveraging Support Vector Machine (Svm) On Small Datasets With Class Imbalance, Akshatha Fakkeriah Kallappanamatt
Application Of Synthetic Informative Minority Over-Sampling (Simo) Algorithm Leveraging Support Vector Machine (Svm) On Small Datasets With Class Imbalance, Akshatha Fakkeriah Kallappanamatt
Dissertations
Developing predictive models for classification problems considering imbalanced datasets is one of the basic difficulties in data mining and decision-analytics. A classifier’s performance will decline dramatically when applied to an imbalanced dataset. Standard classifiers such as logistic regression, Support Vector Machine (SVM) are appropriate for balanced training sets whereas provides suboptimal classification results when used on unbalanced dataset. Performance metric with prediction accuracy encourages a bias towards the majority class, while the rare instances remain unknown though the model contributes a high overall precision. There are chances where minority instances might be treated as noise and vice versa. (Haixiang et …