Improving Software Effort Estimation with Heterogeneous Stacked Ensemble Using SMOTER over ELM and SVR Base Learners
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-36402-0_41
Reference9 articles.
1. De Carvalho, H.D.P., Fagundes, R., Santos, W.: Extreme learning machine applied to software development effort estimation. IEEE Access 9, 92676–92687 (2021). https://doi.org/10.1109/ACCESS.2021.3091313
2. Goyal, S.: Effective software effort estimation using heterogenous stacked ensemble. In: 2022 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), Thiruvananthapuram, India, pp. 584–588 (2022). https://doi.org/10.1109/SPICES52834.2022.9774231
3. Jawa, M., Meena, S.: Software effort estimation using synthetic minority over-sampling technique for regression (SMOTER). In: 2022 3rd International Conference for Emerging Technology (INCET), Belgaum, India, pp. 1–6 (2022). https://doi.org/10.1109/INCET54531.2022.9824043
4. Zakrani, A., Najm, A., Marzak, A.: Support vector regression based on grid-search method for agile software effort prediction. In: 2018 IEEE 5th International Congress on Information Science and Technology (CiSt), Marrakech, Morocco, pp. 1–6 (2018). https://doi.org/10.1109/CIST.2018.8596370
5. Shukla, S., Kumar, S., Bal, P.R.: Analyzing effect of ensemble models on multi-layer perceptron network for software effort estimation. In: 2019 IEEE World Congress on Services (SERVICES), Milan, Italy, pp. 386–387 (2019). https://doi.org/10.1109/SERVICES.2019.00116
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