A Framework for Selecting Machine Learning Models Using TOPSIS

Vazquezl, Maikel Yelandi Leyva; Peñafiel, Luis Andy Briones; Muñoz, Steven Xavier Sanchez; Martínez, Miguel Ángel Quiroz

doi:10.1007/978-3-030-51328-3_18

Cited by 14 publications

(11 citation statements)

References 16 publications

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“…The TOPSIS method, which has been applied in engineering, marketing management and so on, 33 provides an alternative method to select models based on various criteria. 34 In TOPSIS analysis, various dimensional criteria are converted into nondimensional criteria, the positive ideal solution with maximum benefits and minimum costs and the negative ideal solution with minimum benefits and maximum costs are formed, and an alternative is evaluated and selected based on its distance to these solutions. 33 , 35 Therefore, we applied a model selection method based on the AUROC and accuracy of the testing cohort and comprehensively evaluated the performance of the model.…”

Section: Discussionmentioning

confidence: 99%

Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T₂*‐weighted images of cervical spondylotic myelopathy

et al. 2021

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Introduction: Predicting the postoperative neurological function of cervical spondylotic myelopathy (CSM) patients is generally based on conventional magnetic resonance imaging (MRI) patterns, but this approach is not completely satisfactory. This study utilized radiomics, which produced advanced objective and quantitative indicators, and machine learning to develop, validate, test, and compare models for predicting the postoperative prognosis of CSM.Materials and methods: In total, 151 CSM patients undergoing surgical treatment and preoperative MRI was retrospectively collected and divided into good/poor outcome groups based on postoperative modified Japanese Orthopedic Association (mJOA) scores. The datasets obtained from several scanners (an independent scanner) for the training (testing) cohort were used for cross-validation (CV). Radiological models based on the intramedullary hyperintensity and compression ratio were constructed with 14 binary classifiers. Radiomic models based on 237 robust radiomic features were constructed with the same 14 binary classifiers in combination with 7 feature reduction methods, resulting in 98 models. The main outcome measures were the area under the receiver operating characteristic curve (AUROC) and accuracy.Results: Forty-one (11) radiomic models were superior to random guessing during CV (testing), with significant increased AUROC and/or accuracy (P AUROC < .05 and/or P accuracy < .05). One radiological model performed better than random guessing during CV (P accuracy < .05). In the testing cohort, the linear SVM preprocessor + SVM, the best radiomic model (AUROC: 0.74 ± 0.08, accuracy: 0.73 ± 0.07), overperformed the best radiological model (P AUROC = .048). Conclusion: Radiomic features can predict postoperative spinal cord function in CSMpatients. The linear SVM preprocessor + SVM has great application potential in building radiomic models.Meng-Ze Zhang and Han-Qiang Ou-Yang contributed equally to this work.

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Section: Discussionmentioning

confidence: 99%

Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T₂*‐weighted images of cervical spondylotic myelopathy

et al. 2021

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“…In this study, the ranking of the various LSTM architectures was accomplished through A-TOPSIS, an Alternative Technique for Order Preference by Similarity to Ideal Solution [53]. The A-TOPSIS algorithm has been used in literature to compare the performance of several ML classification algorithms [54,55]. Information can get into, stay in, or be read from the cell by using the control gates and memory cell, presented by the following equations:…”

Section: Long Short-term Memorymentioning

confidence: 99%

Predicting Discharges in Sewer Pipes Using an Integrated Long Short-Term Memory and Entropy A-TOPSIS Modeling Framework

Nguyen

Tornyeviadzi

Bui

et al. 2022

Water

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Predicting discharges in sewage systems play an essential role in reducing sewer overflows and impacts on the environment and public health. Choosing a suitable model to predict discharges in these systems is essential to realizing these aforementioned goals. Long Short-Term Memory (LSTM) has been proposed as a robust technique for predicting discharges in wastewater networks. This study explored the potential application of an LSTM model to predict discharges using 3-month data set in a sewer network in Ålesund city, Norway. Different sequence-to-sequence LSTMs were investigated using various input and output datasets. The impact of data aggregation (10-min and 30-min intervals) was examined and compared to original sensor data (5-min intervals) to evaluate the performance of the LSTM model. The results show that 50-neuron LSTM architecture performed better (MAPE = 0.09, RMSE = 0.0008, R2 = 0.8) in predicting discharges for the study area. The study indicates that using the same sequence length for the prior and the forecast can improve the effectiveness of the LSTM model. Based on the results, using a 10-min aggregated discharge dataset reduces energy consumption, transmission bandwidth, and storage capacity. Additionally, it improves prediction performance compared to an original 5-min interval data in Ålesund city.

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“…The potential solutions need to accept input spatial data relevant to the defined spatial problems, while allowing the chosen metrics to measure outputs in a manner that is comparable across different potential solutions. Thus, a few considerations for potential solutions include [104,160,161]:…”

Section: Key Consideration 3: Potential Solutionsmentioning

confidence: 99%

Spatial Decision Support Systems with Automated Machine Learning: A Review

Wen¹,

Li²

2022

Preprint

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Many spatial decision support systems suffer from user adoption issues in practice due to lack of trust, technical expertise, and resources. Automated machine learning has recently allowed non-experts to explore and apply machine learning models in the industry without requiring abundant expert knowledge and resources. This paper reviews recent literature from 136 papers, and proposes a general framework for integrating spatial decision support systems with automated machine learning to lower major user adoption barriers. Challenges of data quality, model interpretability, and practical usefulness were discussed as general considerations for system implementation. Research opportunities related to spatially explicit models in AutoML, and resource-aware, collaborative/connected, and human-centered systems were also discussed to address these challenges. This paper argues that integrating spatial decision support systems with automated machine learning can not only encourage user adoption, but also mutually benefit research in both fields — bridging human-related and technical advancements for fostering future developments in spatial decision support systems and automated machine learning.

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A Framework for Selecting Machine Learning Models Using TOPSIS

Cited by 14 publications

References 16 publications

Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T₂*‐weighted images of cervical spondylotic myelopathy

Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T₂*‐weighted images of cervical spondylotic myelopathy

Predicting Discharges in Sewer Pipes Using an Integrated Long Short-Term Memory and Entropy A-TOPSIS Modeling Framework

Spatial Decision Support Systems with Automated Machine Learning: A Review

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A Framework for Selecting Machine Learning Models Using TOPSIS

Cited by 14 publications

References 16 publications

Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T2*‐weighted images of cervical spondylotic myelopathy

Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T2*‐weighted images of cervical spondylotic myelopathy

Predicting Discharges in Sewer Pipes Using an Integrated Long Short-Term Memory and Entropy A-TOPSIS Modeling Framework

Spatial Decision Support Systems with Automated Machine Learning: A Review

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Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T₂*‐weighted images of cervical spondylotic myelopathy

Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T₂*‐weighted images of cervical spondylotic myelopathy