A hybrid fuzzy feature selection algorithm for high-dimensional regression problems: An mRMR-based framework

Aghaeipoor, Fatemeh; Javidi, Mohammad Masoud

doi:10.1016/j.eswa.2020.113859

Cited by 28 publications

(4 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since embedded methods often consider various characteristics of data such as the local manifold structure, they provide better performance in feature selection compared to the filter and wrapper methods [33]. Hybrid models are constructed by the incorporation of the filter-based techniques into wrapper-based methods aiming to use the advantages of both models [34,35].…”

Section: Dimensionality Reductionmentioning

confidence: 99%

Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods

Saberi-Movahed

Mohammadifard

Mehrpooya

et al. 2021

Preprint

View full text Add to dashboard Cite

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases.

show abstract

Section: Dimensionality Reductionmentioning

confidence: 99%

Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods

Saberi-Movahed

Mohammadifard

Mehrpooya

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Unfortunately, little theoretical justification is provided for the new fitness functions which raises the issue of reliability. Fuzzy-based hybrid algorithms attempt to apply the concepts of fuzzy logic to feature selection [3] , [48] . As with the previous approaches, the added complexity of the fuzzy-based methods does not justify the incremental improvements in performance.…”

Section: Literaturementioning

confidence: 99%

Nested ensemble selection: An effective hybrid feature selection method

Kamalov,

Sulieman,

Moussa

et al. 2023

Heliyon

View full text Add to dashboard Cite

“… Wei et al (2020) found useful texture features in CT images of COVID-19 by mRMR technology and the prediction model showed a good predictive performance. Aghaeipoor and Javidi (2020) put forward the algorithm based on the mRMR framework to improve the accuracy of the estimation method for real-world regression datasets. Özyurt (2020) applied a feature selection algorithm based on CNN–mRMR and an extreme learning Machine (ELM) classifier to achieve a higher classification accuracy for white blood cell detection.…”

Section: Introductionmentioning

confidence: 99%

A New Strategy for Identification of Coal Miners With Abnormal Physical Signs Based on EN-mRMR

Zhou

Bian

et al. 2022

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

Coal miners’ occupational health is a key part of production safety in the coal mine. Accurate identification of abnormal physical signs is the key to preventing occupational diseases and improving miners’ working environment. There are many problems when evaluating the physical health status of miners manually, such as too many sign parameters, low diagnostic efficiency, missed diagnosis, and misdiagnosis. To solve these problems, the machine learning algorithm is used to identify miners with abnormal signs. We proposed a feature screening strategy of integrating elastic net (EN) and Max-Relevance and Min-Redundancy (mRMR) to establish the model to identify abnormal signs and obtain the key physical signs. First, the raw 21 physical signs were expanded to 25 by feature construction technology. Then, the EN was used to delete redundant physical signs. Finally, the mRMR combined with the support vector classification of intelligent optimization algorithm by Gravitational Search Algorithm (GSA-SVC) is applied to further simplify the rest of 12 relatively important physical signs and obtain the optimal model with data of six physical signs. At this time, the accuracy, precision, recall, specificity, G-mean, and MCC of the test set were 97.50%, 97.78%, 97.78%, 97.14%, 0.98, and 0.95. The experimental results show that the proposed strategy improves the model performance with the smallest features and realizes the accurate identification of abnormal coal miners. The conclusion could provide reference evidence for intelligent classification and assessment of occupational health in the early stage.

show abstract

A hybrid fuzzy feature selection algorithm for high-dimensional regression problems: An mRMR-based framework

Cited by 28 publications

References 30 publications

Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods

Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods

Nested ensemble selection: An effective hybrid feature selection method

A New Strategy for Identification of Coal Miners With Abnormal Physical Signs Based on EN-mRMR

Contact Info

Product

Resources

About