Automated Retraining of Machine Learning Models
Akanksha Kavikondala1, Vivek Muppalla2, Krishna Prakasha K3, Vasundhara Acharya4
1Akanksha Kavikondala, Department of Information and Communication Technology, Manipal Institute of Technology (Manipal Academy of Higher Education), Manipal, India.
2Vivek Muppalla, Department of Information and Communication Technology, Manipal Institute of Technology (Manipal Academy of Higher Education), Manipal, India.
3Krishna Prakasha K*, Department of Information and Communication Technology, Manipal Institute of Technology (Manipal Academy of Higher Education), Manipal, India.
4Vasundhara Acharya, Department of Computer Science & Engineering Manipal Institute of Technology (Manipal Academy of Higher Education), Manipal, India.
Manuscript received on September 18, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 445-452 | Volume-8 Issue-12, October 2019. | Retrieval Number: L33221081219/2019©BEIESP | DOI: 10.35940/ijitee.L3322.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Data is the most crucial component of a successful ML system. Once a machine learning model is developed, it gets obsolete over time due to presence of new input data being generated every second. In order to keep our predictions accurate we need to find a way to keep our models up to date. Our research work involves finding a mechanism which can retrain the model with new data automatically. This research also involves exploring the possibilities of automating machine learning processes. We started this project by training and testing our model using conventional machine learning methods. The outcome was then compared with the outcome of those experiments conducted using the AutoML methods like TPOT. This helped us in finding an efficient technique to retrain our models. These techniques can be used in areas where people do not deal with the actual working of a ML model but only require the outputs of ML processes..
Keywords: AutoML, Data preprocessing, Feature extraction, Feature Selection, Hyperparameter, Machine Learning Optimization, Pipeline, Retraining, TPOT
Scope of the Article: Machine Learning