Core Classifier Algorithm: A Hybrid Classification Algorithm Based on Class Core and Clustering
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 (C...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-03-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/7/3524 |
| _version_ | 1797440396073107456 |
|---|---|
| author | Abdalraouf Alarbi Zafer Albayrak |
| author_facet | Abdalraouf Alarbi Zafer Albayrak |
| author_sort | Abdalraouf Alarbi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T12:06:36Z |
| format | Article |
| id | doaj.art-526349c1e8594ed9815a6c8e14a5903c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T12:06:36Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-526349c1e8594ed9815a6c8e14a5903c2023-11-30T22:56:54ZengMDPI AGApplied Sciences2076-34172022-03-01127352410.3390/app12073524Core Classifier Algorithm: A Hybrid Classification Algorithm Based on Class Core and ClusteringAbdalraouf Alarbi0Zafer Albayrak1Department of Computer Engineering, Karabuk University, Karabuk 78000, TurkeyDepartment of Computer Engineering, Sakarya University of Applied Sciences, Sakarya 54100, TurkeyMachine 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.https://www.mdpi.com/2076-3417/12/7/3524classificationphishing attacksK-meanshybridization |
| spellingShingle | Abdalraouf Alarbi Zafer Albayrak Core Classifier Algorithm: A Hybrid Classification Algorithm Based on Class Core and Clustering Applied Sciences classification phishing attacks K-means hybridization |
| title | Core Classifier Algorithm: A Hybrid Classification Algorithm Based on Class Core and Clustering |
| title_full | Core Classifier Algorithm: A Hybrid Classification Algorithm Based on Class Core and Clustering |
| title_fullStr | Core Classifier Algorithm: A Hybrid Classification Algorithm Based on Class Core and Clustering |
| title_full_unstemmed | Core Classifier Algorithm: A Hybrid Classification Algorithm Based on Class Core and Clustering |
| title_short | Core Classifier Algorithm: A Hybrid Classification Algorithm Based on Class Core and Clustering |
| title_sort | core classifier algorithm a hybrid classification algorithm based on class core and clustering |
| topic | classification phishing attacks K-means hybridization |
| url | https://www.mdpi.com/2076-3417/12/7/3524 |
| work_keys_str_mv | AT abdalraoufalarbi coreclassifieralgorithmahybridclassificationalgorithmbasedonclasscoreandclustering AT zaferalbayrak coreclassifieralgorithmahybridclassificationalgorithmbasedonclasscoreandclustering |