Incremental Ant-Miner Classifier for Online Big Data Analytics
Internet of Things (IoT) environments produce large amounts of data that are challenging to analyze. The most challenging aspect is reducing the quantity of consumed resources and time required to retrain a machine learning model as new data records arrive. Therefore, for big data analytics in IoT e...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/6/2223 |
| _version_ | 1797442347928125440 |
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| author | Amal Al-Dawsari Isra Al-Turaiki Heba Kurdi |
| author_facet | Amal Al-Dawsari Isra Al-Turaiki Heba Kurdi |
| author_sort | Amal Al-Dawsari |
| collection | DOAJ |
| description | Internet of Things (IoT) environments produce large amounts of data that are challenging to analyze. The most challenging aspect is reducing the quantity of consumed resources and time required to retrain a machine learning model as new data records arrive. Therefore, for big data analytics in IoT environments where datasets are highly dynamic, evolving over time, it is highly advised to adopt an online (also called incremental) machine learning model that can analyze incoming data instantaneously, rather than an offline model (also called static), that should be retrained on the entire dataset as new records arrive. The main contribution of this paper is to introduce the Incremental Ant-Miner (IAM), a machine learning algorithm for online prediction based on one of the most well-established machine learning algorithms, Ant-Miner. IAM classifier tackles the challenge of reducing the time and space overheads associated with the classic offline classifiers, when used for online prediction. IAM can be exploited in managing dynamic environments to ensure timely and space-efficient prediction, achieving high accuracy, precision, recall, and F-measure scores. To show its effectiveness, the proposed IAM was run on six different datasets from different domains, namely horse colic, credit cards, flags, ionosphere, and two breast cancer datasets. The performance of the proposed model was compared to ten state-of-the-art classifiers: naive Bayes, logistic regression, multilayer perceptron, support vector machine, K*, adaptive boosting (AdaBoost), bagging, Projective Adaptive Resonance Theory (PART), decision tree (C4.5), and random forest. The experimental results illustrate the superiority of IAM as it outperformed all the benchmarks in nearly all performance measures. Additionally, IAM only needs to be rerun on the new data increment rather than the entire big dataset on the arrival of new data records, which makes IAM better in time- and resource-saving. These results demonstrate the strong potential and efficiency of the IAM classifier for big data analytics in various areas. |
| first_indexed | 2024-03-09T12:40:31Z |
| format | Article |
| id | doaj.art-1d5ac0b978734d03a41f4e8815e0937d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T12:40:31Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-1d5ac0b978734d03a41f4e8815e0937d2023-11-30T22:17:56ZengMDPI AGSensors1424-82202022-03-01226222310.3390/s22062223Incremental Ant-Miner Classifier for Online Big Data AnalyticsAmal Al-Dawsari0Isra Al-Turaiki1Heba Kurdi2Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi ArabiaInformation Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi ArabiaComputer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi ArabiaInternet of Things (IoT) environments produce large amounts of data that are challenging to analyze. The most challenging aspect is reducing the quantity of consumed resources and time required to retrain a machine learning model as new data records arrive. Therefore, for big data analytics in IoT environments where datasets are highly dynamic, evolving over time, it is highly advised to adopt an online (also called incremental) machine learning model that can analyze incoming data instantaneously, rather than an offline model (also called static), that should be retrained on the entire dataset as new records arrive. The main contribution of this paper is to introduce the Incremental Ant-Miner (IAM), a machine learning algorithm for online prediction based on one of the most well-established machine learning algorithms, Ant-Miner. IAM classifier tackles the challenge of reducing the time and space overheads associated with the classic offline classifiers, when used for online prediction. IAM can be exploited in managing dynamic environments to ensure timely and space-efficient prediction, achieving high accuracy, precision, recall, and F-measure scores. To show its effectiveness, the proposed IAM was run on six different datasets from different domains, namely horse colic, credit cards, flags, ionosphere, and two breast cancer datasets. The performance of the proposed model was compared to ten state-of-the-art classifiers: naive Bayes, logistic regression, multilayer perceptron, support vector machine, K*, adaptive boosting (AdaBoost), bagging, Projective Adaptive Resonance Theory (PART), decision tree (C4.5), and random forest. The experimental results illustrate the superiority of IAM as it outperformed all the benchmarks in nearly all performance measures. Additionally, IAM only needs to be rerun on the new data increment rather than the entire big dataset on the arrival of new data records, which makes IAM better in time- and resource-saving. These results demonstrate the strong potential and efficiency of the IAM classifier for big data analytics in various areas.https://www.mdpi.com/1424-8220/22/6/2223machine learningassociation rule miningant colony optimizationincremental classifierbig data analyticsIoT |
| spellingShingle | Amal Al-Dawsari Isra Al-Turaiki Heba Kurdi Incremental Ant-Miner Classifier for Online Big Data Analytics Sensors machine learning association rule mining ant colony optimization incremental classifier big data analytics IoT |
| title | Incremental Ant-Miner Classifier for Online Big Data Analytics |
| title_full | Incremental Ant-Miner Classifier for Online Big Data Analytics |
| title_fullStr | Incremental Ant-Miner Classifier for Online Big Data Analytics |
| title_full_unstemmed | Incremental Ant-Miner Classifier for Online Big Data Analytics |
| title_short | Incremental Ant-Miner Classifier for Online Big Data Analytics |
| title_sort | incremental ant miner classifier for online big data analytics |
| topic | machine learning association rule mining ant colony optimization incremental classifier big data analytics IoT |
| url | https://www.mdpi.com/1424-8220/22/6/2223 |
| work_keys_str_mv | AT amalaldawsari incrementalantminerclassifierforonlinebigdataanalytics AT israalturaiki incrementalantminerclassifierforonlinebigdataanalytics AT hebakurdi incrementalantminerclassifierforonlinebigdataanalytics |