Anzeige
Erschienen in:
10.10.2018 | Special Issue
Imbalanced data classification algorithm with support vector machine kernel extensions
Erschienen in: Evolutionary Intelligence | Ausgabe 3/2019
EinloggenAktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
Learning from the imbalanced data samples so as to achieve accurate classification is an important research content in data mining field. It is very difficult for classification algorithm to achieve a higher accuracy because the uneven distribution of data samples makes some categories have few samples. A imbalanced data classification algorithm of support vector machines (KE-SVM) is proposed in this article, this algorithm achieve the initial classification of data samples by training the maximum margin classification SVM model, and then obtaining a new kernel extension function. based on Chi square test and weight coefficient calculation, through training the samples again by the new vector machine with kernel function to improve the classification accuracy. Through the simulation experiments of real data sets of artificial data set, it shows that the proposed method has higher classification accuracy and faster convergence for the uneven distribution data.