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Original Article
Comparative study on classification techniques through IRIS Data Analysis
Susmita Mondal1
Aryapriya Roy2
Sk Wasim Akram3
Sk Md Zakir4
Ankur Biswas5
Kaustuv Bhattacharjee, Anirban Das6
1234567 Department Of Computer Application, University of Engineering & Management, Kolkata, West Bengal, India.
Published Online: March-April 2024
Pages: 82-87
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20240402015
References
1. EN.WIKIPEDIA.ORG/WIKI/IRIS_FLOWER_DATA_SET
2. Asmita Shukla, Ankita Agarwal, Hemlata Pant, and Priyanka Mishra, “Flower Classification using Supervised Learning,”Int. J. Eng.
Res., vol. V9, no. 05, pp. 757–762, 2020.
3. K. Thirunavukkarasu, A. S. Singh, P. Rai, and S. Gupta, “Classification of IRIS dataset using classification based KNN Algorithm in
supervised learning,” 2018 4th Int. Conf. Comput. Commun. Autom. ICCCA 2018, pp. 1–4, 2018.
4. Roe, B. P., Yang, H. J., Zhu, J., Liu, Y., Stancu, I., McGregor, G.: Boosted decision trees as an alternative to artificial neural networks
for particle identification. Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and
Associated Equipment 543(2-3), 577-584 (2005)
5. Iverson, L. R., Prasad, A. M., Matthews, S. N., Peters, M.: Estimating potential habitat for 134 eastern us tree species under six climate
scenarios. Forest Ecology and Management 254(3), 390-406 (2008)
6. Liaw, A., Wiener, M.: Classification and regression by randomForest. R news 2(3), 18-22 (2002)
7. Breiman, L.: Random forests. Machine learning 45(1), 5-32 (2001)
2. Asmita Shukla, Ankita Agarwal, Hemlata Pant, and Priyanka Mishra, “Flower Classification using Supervised Learning,”Int. J. Eng.
Res., vol. V9, no. 05, pp. 757–762, 2020.
3. K. Thirunavukkarasu, A. S. Singh, P. Rai, and S. Gupta, “Classification of IRIS dataset using classification based KNN Algorithm in
supervised learning,” 2018 4th Int. Conf. Comput. Commun. Autom. ICCCA 2018, pp. 1–4, 2018.
4. Roe, B. P., Yang, H. J., Zhu, J., Liu, Y., Stancu, I., McGregor, G.: Boosted decision trees as an alternative to artificial neural networks
for particle identification. Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and
Associated Equipment 543(2-3), 577-584 (2005)
5. Iverson, L. R., Prasad, A. M., Matthews, S. N., Peters, M.: Estimating potential habitat for 134 eastern us tree species under six climate
scenarios. Forest Ecology and Management 254(3), 390-406 (2008)
6. Liaw, A., Wiener, M.: Classification and regression by randomForest. R news 2(3), 18-22 (2002)
7. Breiman, L.: Random forests. Machine learning 45(1), 5-32 (2001)
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