A Novel Classification of Uncertain Stream Data using Ant Colony Optimization Based on Radial Basis Function
There are many potential sources of data uncertainty, such as imperfect measurement or sampling, intrusive environmental monitoring, unreliable sensor networks, and inaccurate medical diagnoses. To avoid unintended results, data mining from new applications like sensors and location-based services n...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Sulaimani Polytechnic University
2022-11-01
|
| Series: | Kurdistan Journal of Applied Research |
| Subjects: | |
| Online Access: | https://www.kjar.spu.edu.iq/index.php/kjar/article/view/787 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823857409690435584 |
|---|---|
| author | Tahsin Ali Mohammed Amin Sabah Robitan Mahmood Rebar Dara Mohammed Pshtiwan Jabar Karim |
| author_facet | Tahsin Ali Mohammed Amin Sabah Robitan Mahmood Rebar Dara Mohammed Pshtiwan Jabar Karim |
| author_sort | Tahsin Ali Mohammed Amin |
| collection | DOAJ |
| description | There are many potential sources of data uncertainty, such as imperfect measurement or sampling, intrusive environmental monitoring, unreliable sensor networks, and inaccurate medical diagnoses. To avoid unintended results, data mining from new applications like sensors and location-based services needs to be done with care. When attempting to classify data with a high degree of uncertainty, many researchers have turned to heuristic approaches and machine learning (ML) methods. We propose an entirely new ML method in this paper by fusing the Radial Basis Function (RBF) network based on ant colony optimization (ACO). After introducing a large amount of uncertainty into a dataset, we normalize the data and finish training on clean data. The ant colony optimization algorithm is then used to train a recurrent neural network. Finally, we evaluate our proposed method against some of the most popular ML methods, including a k-nearest neighbor, support vector machine, random forest, decision tree, logistic regression, and extreme gradient boosting (Xgboost). Error metrics show that our model significantly outperforms the gold standard and other popular ML methods. Using industry-standard performance metrics, the results of our experiments show that our proposed method does a better job of classifying uncertain data than other methods
|
| format | Article |
| id | doaj-art-3abc297104f84f2597ed4230179e8208 |
| institution | Kabale University |
| issn | 2411-7684 2411-7706 |
| language | English |
| publishDate | 2022-11-01 |
| publisher | Sulaimani Polytechnic University |
| record_format | Article |
| series | Kurdistan Journal of Applied Research |
| spelling | doaj-art-3abc297104f84f2597ed4230179e82082025-02-11T21:00:07ZengSulaimani Polytechnic UniversityKurdistan Journal of Applied Research2411-76842411-77062022-11-017210.24017/Science.2022.2.5787A Novel Classification of Uncertain Stream Data using Ant Colony Optimization Based on Radial Basis FunctionTahsin Ali Mohammed Amin0Sabah Robitan Mahmood1Rebar Dara Mohammed2Pshtiwan Jabar Karim3Department of Database Technology Technical College of Informatics Sulaimani Polytechnic University Sulaymaniyah, IraqDepartment of Information Technology Technical College of Informatics Sulaimani Polytechnic University Sulaymaniyah, IraqDepartment of Database Technology Technical College of Informatics Sulaimani Polytechnic University Sulaymaniyah, IraqDepartment of Computer Science College of Science University of Garmian Kalar, IraqThere are many potential sources of data uncertainty, such as imperfect measurement or sampling, intrusive environmental monitoring, unreliable sensor networks, and inaccurate medical diagnoses. To avoid unintended results, data mining from new applications like sensors and location-based services needs to be done with care. When attempting to classify data with a high degree of uncertainty, many researchers have turned to heuristic approaches and machine learning (ML) methods. We propose an entirely new ML method in this paper by fusing the Radial Basis Function (RBF) network based on ant colony optimization (ACO). After introducing a large amount of uncertainty into a dataset, we normalize the data and finish training on clean data. The ant colony optimization algorithm is then used to train a recurrent neural network. Finally, we evaluate our proposed method against some of the most popular ML methods, including a k-nearest neighbor, support vector machine, random forest, decision tree, logistic regression, and extreme gradient boosting (Xgboost). Error metrics show that our model significantly outperforms the gold standard and other popular ML methods. Using industry-standard performance metrics, the results of our experiments show that our proposed method does a better job of classifying uncertain data than other methods https://www.kjar.spu.edu.iq/index.php/kjar/article/view/787Uncertainty dataMachine LearningRadial Basis FunctionAnt Colony Optimization (ACO)IoT Applications |
| spellingShingle | Tahsin Ali Mohammed Amin Sabah Robitan Mahmood Rebar Dara Mohammed Pshtiwan Jabar Karim A Novel Classification of Uncertain Stream Data using Ant Colony Optimization Based on Radial Basis Function Kurdistan Journal of Applied Research Uncertainty data Machine Learning Radial Basis Function Ant Colony Optimization (ACO) IoT Applications |
| title | A Novel Classification of Uncertain Stream Data using Ant Colony Optimization Based on Radial Basis Function |
| title_full | A Novel Classification of Uncertain Stream Data using Ant Colony Optimization Based on Radial Basis Function |
| title_fullStr | A Novel Classification of Uncertain Stream Data using Ant Colony Optimization Based on Radial Basis Function |
| title_full_unstemmed | A Novel Classification of Uncertain Stream Data using Ant Colony Optimization Based on Radial Basis Function |
| title_short | A Novel Classification of Uncertain Stream Data using Ant Colony Optimization Based on Radial Basis Function |
| title_sort | novel classification of uncertain stream data using ant colony optimization based on radial basis function |
| topic | Uncertainty data Machine Learning Radial Basis Function Ant Colony Optimization (ACO) IoT Applications |
| url | https://www.kjar.spu.edu.iq/index.php/kjar/article/view/787 |
| work_keys_str_mv | AT tahsinalimohammedamin anovelclassificationofuncertainstreamdatausingantcolonyoptimizationbasedonradialbasisfunction AT sabahrobitanmahmood anovelclassificationofuncertainstreamdatausingantcolonyoptimizationbasedonradialbasisfunction AT rebardaramohammed anovelclassificationofuncertainstreamdatausingantcolonyoptimizationbasedonradialbasisfunction AT pshtiwanjabarkarim anovelclassificationofuncertainstreamdatausingantcolonyoptimizationbasedonradialbasisfunction AT tahsinalimohammedamin novelclassificationofuncertainstreamdatausingantcolonyoptimizationbasedonradialbasisfunction AT sabahrobitanmahmood novelclassificationofuncertainstreamdatausingantcolonyoptimizationbasedonradialbasisfunction AT rebardaramohammed novelclassificationofuncertainstreamdatausingantcolonyoptimizationbasedonradialbasisfunction AT pshtiwanjabarkarim novelclassificationofuncertainstreamdatausingantcolonyoptimizationbasedonradialbasisfunction |