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A combined approach of base and meta learners for hybrid system

Year 2023, Volume: 7 Issue: 1, 25 - 32, 15.01.2023

Abdul Ahad Abro Waqas Ahmed Sıddıque Mir Sajjad Hussain Talpur Awais Khan Jumani Erkan Yaşar

https://doi.org/10.31127/tuje.1007508
Cited By: 3

Abstract

The ensemble learning method is considered a meaningful yet challenging task. To enhance the performance of binary classification and predictive analysis, this paper proposes an effective ensemble learning approach by applying multiple models to produce efficient and effective outcomes. In these experimental studies, three base learners, J48, Multilayer Perceptron (MP), and Support Vector Machine (SVM) are being utilized. Moreover, two meta-learners, Bagging and Rotation Forest are being used in this analysis. Firstly, to produce effective results and capture productive data, the base learner, the J48 decision tree is aggregated with the rotation forest. Secondly, machine learning and ensemble learning classification algorithms along with the five UCI Datasets are being applied to progress the robustness of the system. Whereas, the recommended mechanism is evaluated by implementing five performance standards concerning the accuracy, AUC (Area Under Curve), precision, recall and F-measure values. In this regard, extensive strategies and various approaches were being studied and applied to obtain improved results from the current literature; however, they were insufficient to provide successful results. We present experimental results which demonstrate the efficiency of our approach to well-known competitive approaches. This method can be applied to image identification and machine learning problems, such as binary classification.

Keywords

Artificial Intelligence Machine Learning Ensemble Learning Pattern Recognition Data Mining Classification

References

  • Urso, A., Fiannaca, A., La Rosa, M., Ravì, V., & Rizzo, R. (2018). Data mining: Classification and prediction. Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, 1, 3, 384-402.
  • Galdi, P., & Tagliaferri, R. (2018). Data mining: accuracy and error measures for classification and prediction. Encyclopedia of Bioinformatics and Computational Biology, 431-436.
  • Ozcift, A., & Gulten, A. (2011). Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms. Computer Methods and Programs in Biomedicine, 104(3), 443-451.
  • Panigrahi, R., & Borah, S. (2018). Rank allocation to J48 group of decision tree classifiers using binary and multiclass intrusion detection datasets. Procedia Computer Science, 132, 323-332.
  • Bansal, D., Chhikara, R., Khanna, K., & Gupta, P. (2018). Comparative analysis of various machine learning algorithms for detecting dementia. Procedia Computer Science, 132, 1497-1502.
  • Zhang, C. X., & Zhang, J. S. (2008). RotBoost: A technique for combining Rotation Forest and AdaBoost. Pattern Recognition Letters, 29(10), 1524-1536.
  • Chen, S. F., Gu, H., Tu, M. Y., Zhou, Y. P., & Cui, Y. F. (2018). Robust variable selection based on bagging classification tree for support vector machine in metabonomic data analysis. Journal of Chemometrics, 32(11), e2921.
  • Lamba, R., Gulati, T., Alharbi, H. F., & Jain, A. (2021). A hybrid system for Parkinson’s disease diagnosis using machine learning techniques. International Journal of Speech Technology, 1-11.
  • Nandhini, M. (2021). Ensemble human movement sequence prediction model with Apriori based Probability Tree Classifier (APTC) and Bagged J48 on Machine learning. Journal of King Saud University-Computer and Information Sciences, 33(4), 408-416.
  • Pham, B. T., Bui, D. T., Prakash, I., & Dholakia, M. B. (2017). Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. Catena, 149, 52-63.
  • Sun, X., Xu, J., Jiang, C., Feng, J., Chen, S. S., & He, F. (2016). Extreme learning machine for multi-label classification. Entropy, 18(6), 225.
  • Utkin, L. V. (2019). An imprecise extension of SVM-based machine learning models. Neurocomputing, 331, 18-32.
  • Abro, A. A., Taşci, E., & Aybars, U. (2020). A Stacking-based Ensemble Learning Method for Outlier Detection. Balkan Journal of Electrical and Computer Engineering, 8(2), 181-185.
  • Chen, T. (2017). An improved rotation forest algorithm based on heterogeneous classifiers ensemble for classifying gene expression profile. Advances in Modelling and Analysis B, 60(1), 1-24.
  • Khan, A. A., Shaikh, Z. A., Belinskaja, L., Baitenova, L., Vlasova, Y., Gerzelieva Z, Laghari A. A., Abro, A.A., & Barykin, S. (2022). A Blockchain and Metaheuristic-Enabled Distributed Architecture for Smart Agricultural Analysis and Ledger Preservation Solution: A Collaborative Approach. Applied Sciences, 12(3), 1487.
  • Abro, A. A., Khan, A. A., Talpur, M. S. H., & Kayijuka, I. (2021). Machine Learning Classifiers: A Brief Primer. University of Sindh Journal of Information and Communication Technology, 5(2), 63-68.
  • Lu, H., Yang, L., Yan, K., Xue, Y., & Gao, Z. (2017). A cost-sensitive rotation forest algorithm for gene expression data classification. Neurocomputing, 228, 270 -276.
  • Olivares, R., Munoz, R., Soto, R., Crawford, B., Cárdenas, D., Ponce, A., & Taramasco, C. (2020). An optimized brain-based algorithm for classifying Parkinson’s disease. Applied Sciences, 10(5), 1827.
  • Khan, A. A., Laghari, A. A., & Awan, S. A. (2021). Machine learning in computer vision: A review. EAI Transactions on Scalable Information Systems, e4.
  • Shuaib, M., Abdulhamid, S. I. M., Adebayo, O. S., Osho, O., Idris, I., Alhassan, J. K., & Rana, N. (2019). Whale optimization algorithm-based email spam feature selection method using rotation forest algorithm for classification. SN Applied Sciences, 1(5), 1-17.
  • Hong, H., Liu, J., Bui, D. T., Pradhan, B., Acharya, T. D., Pham, B. T., ... & Ahmad, B. B. (2018). Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China). Catena, 163, 399-413.
  • Abro, A. A., Yimer, M. A., & Bhatti, Z. (2020). Identifying the Machine Learning Techniques for Classification of Target Datasets. Sukkur IBA Journal of Computing and Mathematical Sciences, 4(1), 45-52.
  • Singh, B. K., Verma, K., & Thoke, A. S. (2015). Investigations on impact of feature normalization techniques on classifier's performance in breast tumor classification. International Journal of Computer Applications, 116(19).
  • Abro, A. A. (2021). Vote-Based: Ensemble Approach. Sakarya University Journal of Science, 25(3), 858-866.
  • UCI Machine Learning Repository, 2018, https://archive.ics.uci.edu/ml/index.php.

Year 2023, Volume: 7 Issue: 1, 25 - 32, 15.01.2023

Abdul Ahad Abro Waqas Ahmed Sıddıque Mir Sajjad Hussain Talpur Awais Khan Jumani Erkan Yaşar

https://doi.org/10.31127/tuje.1007508
Cited By: 3

Abstract

References

  • Urso, A., Fiannaca, A., La Rosa, M., Ravì, V., & Rizzo, R. (2018). Data mining: Classification and prediction. Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, 1, 3, 384-402.
  • Galdi, P., & Tagliaferri, R. (2018). Data mining: accuracy and error measures for classification and prediction. Encyclopedia of Bioinformatics and Computational Biology, 431-436.
  • Ozcift, A., & Gulten, A. (2011). Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms. Computer Methods and Programs in Biomedicine, 104(3), 443-451.
  • Panigrahi, R., & Borah, S. (2018). Rank allocation to J48 group of decision tree classifiers using binary and multiclass intrusion detection datasets. Procedia Computer Science, 132, 323-332.
  • Bansal, D., Chhikara, R., Khanna, K., & Gupta, P. (2018). Comparative analysis of various machine learning algorithms for detecting dementia. Procedia Computer Science, 132, 1497-1502.
  • Zhang, C. X., & Zhang, J. S. (2008). RotBoost: A technique for combining Rotation Forest and AdaBoost. Pattern Recognition Letters, 29(10), 1524-1536.
  • Chen, S. F., Gu, H., Tu, M. Y., Zhou, Y. P., & Cui, Y. F. (2018). Robust variable selection based on bagging classification tree for support vector machine in metabonomic data analysis. Journal of Chemometrics, 32(11), e2921.
  • Lamba, R., Gulati, T., Alharbi, H. F., & Jain, A. (2021). A hybrid system for Parkinson’s disease diagnosis using machine learning techniques. International Journal of Speech Technology, 1-11.
  • Nandhini, M. (2021). Ensemble human movement sequence prediction model with Apriori based Probability Tree Classifier (APTC) and Bagged J48 on Machine learning. Journal of King Saud University-Computer and Information Sciences, 33(4), 408-416.
  • Pham, B. T., Bui, D. T., Prakash, I., & Dholakia, M. B. (2017). Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. Catena, 149, 52-63.
  • Sun, X., Xu, J., Jiang, C., Feng, J., Chen, S. S., & He, F. (2016). Extreme learning machine for multi-label classification. Entropy, 18(6), 225.
  • Utkin, L. V. (2019). An imprecise extension of SVM-based machine learning models. Neurocomputing, 331, 18-32.
  • Abro, A. A., Taşci, E., & Aybars, U. (2020). A Stacking-based Ensemble Learning Method for Outlier Detection. Balkan Journal of Electrical and Computer Engineering, 8(2), 181-185.
  • Chen, T. (2017). An improved rotation forest algorithm based on heterogeneous classifiers ensemble for classifying gene expression profile. Advances in Modelling and Analysis B, 60(1), 1-24.
  • Khan, A. A., Shaikh, Z. A., Belinskaja, L., Baitenova, L., Vlasova, Y., Gerzelieva Z, Laghari A. A., Abro, A.A., & Barykin, S. (2022). A Blockchain and Metaheuristic-Enabled Distributed Architecture for Smart Agricultural Analysis and Ledger Preservation Solution: A Collaborative Approach. Applied Sciences, 12(3), 1487.
  • Abro, A. A., Khan, A. A., Talpur, M. S. H., & Kayijuka, I. (2021). Machine Learning Classifiers: A Brief Primer. University of Sindh Journal of Information and Communication Technology, 5(2), 63-68.
  • Lu, H., Yang, L., Yan, K., Xue, Y., & Gao, Z. (2017). A cost-sensitive rotation forest algorithm for gene expression data classification. Neurocomputing, 228, 270 -276.
  • Olivares, R., Munoz, R., Soto, R., Crawford, B., Cárdenas, D., Ponce, A., & Taramasco, C. (2020). An optimized brain-based algorithm for classifying Parkinson’s disease. Applied Sciences, 10(5), 1827.
  • Khan, A. A., Laghari, A. A., & Awan, S. A. (2021). Machine learning in computer vision: A review. EAI Transactions on Scalable Information Systems, e4.
  • Shuaib, M., Abdulhamid, S. I. M., Adebayo, O. S., Osho, O., Idris, I., Alhassan, J. K., & Rana, N. (2019). Whale optimization algorithm-based email spam feature selection method using rotation forest algorithm for classification. SN Applied Sciences, 1(5), 1-17.
  • Hong, H., Liu, J., Bui, D. T., Pradhan, B., Acharya, T. D., Pham, B. T., ... & Ahmad, B. B. (2018). Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China). Catena, 163, 399-413.
  • Abro, A. A., Yimer, M. A., & Bhatti, Z. (2020). Identifying the Machine Learning Techniques for Classification of Target Datasets. Sukkur IBA Journal of Computing and Mathematical Sciences, 4(1), 45-52.
  • Singh, B. K., Verma, K., & Thoke, A. S. (2015). Investigations on impact of feature normalization techniques on classifier's performance in breast tumor classification. International Journal of Computer Applications, 116(19).
  • Abro, A. A. (2021). Vote-Based: Ensemble Approach. Sakarya University Journal of Science, 25(3), 858-866.
  • UCI Machine Learning Repository, 2018, https://archive.ics.uci.edu/ml/index.php.
There are 25 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Abdul Ahad Abro 0000-0002-3591-9231

Waqas Ahmed Sıddıque 0000-0001-8206-4451

Mir Sajjad Hussain Talpur 0000-0001-9897-3916

Awais Khan Jumani 0000-0001-9468-0446

Erkan Yaşar 0000-0001-7333-6320

Publication Date January 15, 2023
Published in Issue Year 2023 Volume: 7 Issue: 1

Cite

APA Abro, A. A., Sıddıque, W. A., Talpur, M. S. H., Jumani, A. K., et al. (2023). A combined approach of base and meta learners for hybrid system. Turkish Journal of Engineering, 7(1), 25-32. https://doi.org/10.31127/tuje.1007508
AMA Abro AA, Sıddıque WA, Talpur MSH, Jumani AK, Yaşar E. A combined approach of base and meta learners for hybrid system. TUJE. January 2023;7(1):25-32. doi:10.31127/tuje.1007508
Chicago Abro, Abdul Ahad, Waqas Ahmed Sıddıque, Mir Sajjad Hussain Talpur, Awais Khan Jumani, and Erkan Yaşar. “A Combined Approach of Base and Meta Learners for Hybrid System”. Turkish Journal of Engineering 7, no. 1 (January 2023): 25-32. https://doi.org/10.31127/tuje.1007508.
EndNote Abro AA, Sıddıque WA, Talpur MSH, Jumani AK, Yaşar E (January 1, 2023) A combined approach of base and meta learners for hybrid system. Turkish Journal of Engineering 7 1 25–32.
IEEE A. A. Abro, W. A. Sıddıque, M. S. H. Talpur, A. K. Jumani, and E. Yaşar, “A combined approach of base and meta learners for hybrid system”, TUJE, vol. 7, no. 1, pp. 25–32, 2023, doi: 10.31127/tuje.1007508.
ISNAD Abro, Abdul Ahad et al. “A Combined Approach of Base and Meta Learners for Hybrid System”. Turkish Journal of Engineering 7/1 (January 2023), 25-32. https://doi.org/10.31127/tuje.1007508.
JAMA Abro AA, Sıddıque WA, Talpur MSH, Jumani AK, Yaşar E. A combined approach of base and meta learners for hybrid system. TUJE. 2023;7:25–32.
MLA Abro, Abdul Ahad et al. “A Combined Approach of Base and Meta Learners for Hybrid System”. Turkish Journal of Engineering, vol. 7, no. 1, 2023, pp. 25-32, doi:10.31127/tuje.1007508.
Vancouver Abro AA, Sıddıque WA, Talpur MSH, Jumani AK, Yaşar E. A combined approach of base and meta learners for hybrid system. TUJE. 2023;7(1):25-32.

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