Optimizing Kernel Transformations to Handle Binary Class Imbalanced Dataset Classification
Imbalanced class distributions pose a prevalent challenge in numerous classification problems, requiring effective strategies for learning from such skewed data. Traditional machine learning algorithms often struggle with imbalanced datasets, as they tend to bias their classification functions towar...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
|
| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2024.2408933 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850116284219392000 |
|---|---|
| author | Vaibhavi Patel Hetal Bhavsar |
| author_facet | Vaibhavi Patel Hetal Bhavsar |
| author_sort | Vaibhavi Patel |
| collection | DOAJ |
| description | Imbalanced class distributions pose a prevalent challenge in numerous classification problems, requiring effective strategies for learning from such skewed data. Traditional machine learning algorithms often struggle with imbalanced datasets, as they tend to bias their classification functions toward the majority class, resulting in suboptimal performance for minority classes. In our research, we propose a novel approach to address this challenge specifically tailored for Support Vector Machines (SVM), a well-established family of learning algorithms. Our method leverages a kernel trick to enhance the SVM’s classification capabilities on imbalanced datasets named KTI. It aims to streamline the classification process by incorporating adaptive data transformations within the algorithm itself, offering a more efficient and integrated solution for handling imbalanced data. Experimental evaluations conducted on diverse real-world datasets demonstrate the superior performance of our proposed strategy compared to existing methods, showcasing its potential for practical applications in classification tasks with skewed class distributions. |
| format | Article |
| id | doaj-art-0d255a69d42c4296a29d6da0150b3f39 |
| institution | OA Journals |
| issn | 0883-9514 1087-6545 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj-art-0d255a69d42c4296a29d6da0150b3f392025-08-20T02:36:22ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452024-12-0138110.1080/08839514.2024.2408933Optimizing Kernel Transformations to Handle Binary Class Imbalanced Dataset ClassificationVaibhavi Patel0Hetal Bhavsar1Department of Computer science and Engineering, The Maharaja Sayajirao University, Vadodara, IndiaDepartment of Computer science and Engineering, The Maharaja Sayajirao University, Vadodara, IndiaImbalanced class distributions pose a prevalent challenge in numerous classification problems, requiring effective strategies for learning from such skewed data. Traditional machine learning algorithms often struggle with imbalanced datasets, as they tend to bias their classification functions toward the majority class, resulting in suboptimal performance for minority classes. In our research, we propose a novel approach to address this challenge specifically tailored for Support Vector Machines (SVM), a well-established family of learning algorithms. Our method leverages a kernel trick to enhance the SVM’s classification capabilities on imbalanced datasets named KTI. It aims to streamline the classification process by incorporating adaptive data transformations within the algorithm itself, offering a more efficient and integrated solution for handling imbalanced data. Experimental evaluations conducted on diverse real-world datasets demonstrate the superior performance of our proposed strategy compared to existing methods, showcasing its potential for practical applications in classification tasks with skewed class distributions.https://www.tandfonline.com/doi/10.1080/08839514.2024.2408933 |
| spellingShingle | Vaibhavi Patel Hetal Bhavsar Optimizing Kernel Transformations to Handle Binary Class Imbalanced Dataset Classification Applied Artificial Intelligence |
| title | Optimizing Kernel Transformations to Handle Binary Class Imbalanced Dataset Classification |
| title_full | Optimizing Kernel Transformations to Handle Binary Class Imbalanced Dataset Classification |
| title_fullStr | Optimizing Kernel Transformations to Handle Binary Class Imbalanced Dataset Classification |
| title_full_unstemmed | Optimizing Kernel Transformations to Handle Binary Class Imbalanced Dataset Classification |
| title_short | Optimizing Kernel Transformations to Handle Binary Class Imbalanced Dataset Classification |
| title_sort | optimizing kernel transformations to handle binary class imbalanced dataset classification |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2024.2408933 |
| work_keys_str_mv | AT vaibhavipatel optimizingkerneltransformationstohandlebinaryclassimbalanceddatasetclassification AT hetalbhavsar optimizingkerneltransformationstohandlebinaryclassimbalanceddatasetclassification |