Automated evolutionary approach for the design of composite machine learning pipelines

Nikitin, Nikolay O.; Vychuzhanin, Pavel; Sarafanov, Mikhail; Polonskaia, Iana S.; Revin, Ilia; Barabanova, Irina V.; Maximov, Gleb; Kalyuzhnaya, Anna V.; Boukhanovsky, Alexander V.

doi:10.1016/j.future.2021.08.022

Cited by 30 publications

(17 citation statements)

References 26 publications

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“…In specific practical settings, results of BN can upgrade the quality of ML models, and CP outcome prediction is one of these tasks. We provided such an outcome by using FEDOT [ 45 ] for predicting the length of stay and the output of DBN for improving the quality of FEDOT’s results. FEDOT is an open-source framework for automated modeling and machine learning (auto ML).…”

Section: Resultsmentioning

confidence: 99%

Hybrid Bayesian Network-Based Modeling: COVID-19-Pneumonia Case

Derevitskii

Mramorov

Usoltsev

et al. 2022

JPM

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The primary goal of this paper is to develop an approach for predicting important clinical indicators, which can be used to improve treatment. Using mathematical predictive modeling algorithms, we examined the course of COVID-19-based pneumonia (CP) with inpatient treatment. Algorithms used include dynamic and ordinary Bayesian networks (OBN and DBN), popular ML algorithms, the state-of-the-art auto ML approach and our new hybrid method based on DBN and auto ML approaches. Predictive targets include treatment outcomes, length of stay, dynamics of disease severity indicators, and facts of prescribed drugs for different time intervals of observation. Models are validated using expert knowledge, current clinical recommendations, preceding research and classic predictive metrics. The characteristics of the best models are as follows: MAE of 3.6 days of predicting LOS (DBN plus FEDOT auto ML framework), 0.87 accuracy of predicting treatment outcome (OBN); 0.98 F1 score for predicting facts of prescribed drug (DBN). Moreover, the advantage of the proposed approach is Bayesian network-based interpretability, which is very important in the medical field. After the validation of other CP datasets for other hospitals, the proposed models can be used as part of the decision support systems for improving COVID-19-based pneumonia treatment. Another important finding is the significant differences between COVID-19 and non-COVID-19 pneumonia.

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Section: Resultsmentioning

confidence: 99%

Hybrid Bayesian Network-Based Modeling: COVID-19-Pneumonia Case

Derevitskii

Mramorov

Usoltsev

et al. 2022

JPM

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“…Composite models (pipelines) show strong predictive performance when processing different types of data [52]. Therefore, to build a precise and at the same time robust model, it was decided to use a composite approach with automatic machine learning techniques and evolutionary computing.…”

Section: Composite Modelling Approachmentioning

confidence: 99%

“…Individual blocks based on machine learning models are identified using the evolutionary algorithm for automatic machine learning described in the paper [52]. Then, the ensembling was used to combine the predictions of the individual blocks in the composite pipelines.…”

Section: Composite Modelling Approachmentioning

confidence: 99%

Short-Term River Flood Forecasting Using Composite Models and Automated Machine Learning: The Case Study of Lena River

Sarafanov

Borisova

Maslyaev

et al. 2021

Water

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The paper presents a hybrid approach for short-term river flood forecasting. It is based on multi-modal data fusion from different sources (weather stations, water height sensors, remote sensing data). To improve the forecasting efficiency, the machine learning methods and the Snowmelt-Runoff physical model are combined in a composite modeling pipeline using automated machine learning techniques. The novelty of the study is based on the application of automated machine learning to identify the individual blocks of a composite pipeline without involving an expert. It makes it possible to adapt the approach to various river basins and different types of floods. Lena River basin was used as a case study since its modeling during spring high water is complicated by the high probability of ice-jam flooding events. Experimental comparison with the existing methods confirms that the proposed approach reduces the error at each analyzed level gauging station. The value of Nash–Sutcliffe model efficiency coefficient for the ten stations chosen for comparison is 0.80. The other approaches based on statistical and physical models could not surpass the threshold of 0.74. Validation for a high-water period also confirms that a composite pipeline designed using automated machine learning is much more efficient than stand-alone models.

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“…Yet, the review did not perform any kind of empirical comparison. More recently, the FEDOT AutoTSF tool was empirically compared against the Facebook Prophet [19] and AutoTS [21] tools, outperforming both in terms of predictive performances for a set of 12 financial time series [15].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we perform a robust benchmark of eight recent AutoTSF tools (a value that is substantially higher than what was executed in [15]), namely: Pmdarima, Prophet, Ludwig (an AutoDL that is adapted here for TSF), DeepAR, TFT, FEDOT, AutoTs and Sktime. To test the tools, nine time series that can be associated with the smart cities context were used.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Automated Time Series Forecasting Tools for Smart Cities

Pereira

Costa²,

Barros³

et al. 2022

Progress in Artificial Intelligence

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Most smart city sensors generate time series records and forecasting such data can provide valuable insights for citizens and city managers. Within this context, the adoption of Automated Time Series Forecasting (AutoTSF) tools is a key issue, since it facilitates the design and deployment of multiple TSF models. In this work, we adapt and compare eight recent AutoTSF tools (Pmdarima, Prophet, Ludwig, DeepAR, TFT, FEDOT, AutoTs and Sktime) using nine freely available time series that can be related with the smart city concept (e.g., temperature, energy consumption, city traffic). An extensive experimentation was carried out by using a realistic rolling window with several training and testing iterations. Also, the AutoTSF tools were evaluated by considering both the predictive performances and required computational effort. Overall, the FEDOT tool presented the best overall performance.

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Automated evolutionary approach for the design of composite machine learning pipelines

Cited by 30 publications

References 26 publications

Hybrid Bayesian Network-Based Modeling: COVID-19-Pneumonia Case

Hybrid Bayesian Network-Based Modeling: COVID-19-Pneumonia Case

Short-Term River Flood Forecasting Using Composite Models and Automated Machine Learning: The Case Study of Lena River

A Comparison of Automated Time Series Forecasting Tools for Smart Cities

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