Core Classifier Algorithm: A Hybrid Classification Algorithm Based on Class Core and Clustering
Küçük Resim Yok
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Mdpi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Machine learning classification algorithms vary drastically in their approaches, and researchers have always been trying to reduce the common boundaries of nonlinear classification, overlapping, or noise. This study summarizes the steps of hybridizing a new algorithm named Core Classify Algorithm (CCA) derived from K-nearest neighbor (KNN) and an unsupervised learning partitioning algorithm (K-means), aiming to avoid the unrepresentative Cores of the clusters while finding the similarities. This hybridization step is meant to harvest the benefits of combining two algorithms by changing results through iteration to obtain the most optimal results and classifying the data according to the labels with two or more clusters with higher accuracy and better computational efficiency. Our new approach was tested on a total of five datasets from two different domains: one phishing URL, three healthcare, and one synthetic dataset. Our results demonstrate that the accuracy of the CCA model in non-linear experiments representing datasets two to five was lower than that of dataset one which represented a linear classification and achieved an accuracy of 100%, equal in rank with Random Forest, Support Vector Machine, and Decision Trees. Moreover, our results also demonstrate that hybridization can be used to exploit flaws in specific algorithms to further improve their performance.
Açıklama
Anahtar Kelimeler
classification, phishing attacks, K-means, hybridization
Kaynak
Applied Sciences-Basel
WoS Q Değeri
Q2
Scopus Q Değeri
Q2
Cilt
12
Sayı
7
