An efficient Pearson correlation based improved random forest classification for protein structure prediction techniques

Kalaiselvi, B.; Thangamani, M.

doi:10.1016/j.measurement.2020.107885

Cited by 30 publications

(11 citation statements)

References 17 publications

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“…Hence, if coefficient value is closer to 0 implies weaker correlation, while zero coefficient value implies no correlation. Pearson coefficient [50], denoted as p c , is defined as:…”

Section: ) Feature Selectionmentioning

confidence: 99%

Machine Learning Tools for Long-Term Type 2 Diabetes Risk Prediction

et al. 2021

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working and living environments supporting active and healthy ageing.ABSTRACT A steady rise has been observed in the percentage of elderly people who want and are still able to contribute to society. Therefore, early retirement or exit from the labour market, due to healthrelated issues, poses a significant problem. Nowadays, thanks to technological advances and various data from different populations, the risk factors investigation and health issues screening are moving towards automation. In the context of this work, a worker-centric, IoT enabled unobtrusive users health, wellbeing and functional ability monitoring framework, empowered with AI tools, is proposed. Diabetes is a high-prevalence chronic condition with harmful consequences for the quality of life and high mortality rate for people worldwide, in both developed and developing countries. Hence, its severe impact on humans' life, e.g., personal, social, working, can be considerably reduced if early detection is possible, but most research works in this field fail to provide a more personalized approach both in the modeling and prediction process. In this direction, our designed system concerns diabetes risk prediction in which specific components of the Knowledge Discovery in Database (KDD) process are applied, evaluated and incorporated. Specifically, dataset creation, features selection and classification, using different Supervised Machine Learning (ML) models are considered. The ensemble WeightedVotingLRRFs ML model is proposed to improve the prediction of diabetes, scoring an Area Under the ROC Curve (AUC) of 0.884. Concerning the weighted voting, the optimal weights are estimated by their corresponding Sensitivity and AUC of the ML model based on a bi-objective genetic algorithm. Also, a comparative study is presented among the Finnish Diabetes Risk Score (FINDRISC) and Leicester risk score systems and several ML models, using inductive and transductive learning. The experiments were conducted using data extracted from the English Longitudinal Study of Ageing (ELSA) database.INDEX TERMS T2DM, long-term health risk prediction, machine learning, ensemble learning

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“…Hence, if coefficient value is closer to 0 implies weaker correlation, while zero coefficient value implies no correlation. Pearson coefficient [50], denoted as p c , is defined as:…”

Section: ) Feature Selectionmentioning

confidence: 99%

Machine Learning Tools for Long-Term Type 2 Diabetes Risk Prediction

et al. 2021

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“…In recent years, ensemble learning can organically combine multiple prediction results obtained by multiple single learning models to obtain more accurate, stable and strong final results. For exampleensemble learning models such as Boosting, Bagging and RF have been proposed one after another and applied to various types of data sets [29,30]. In this study, through the deep learning of the ensemble learning model, six common machine learning classification algorithms have been selected for comparative experiments, including RF, multi-label naive Bayes (NB), and multi-label SVM (SVM), multi-label logistic regression (LR), GCForest.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence Applications in allergic rhinitis diagnosis: Focus on Ensemble Learning

Dai¹,

Meng

Tan

et al. 2020

Preprint

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Background and purpose: Artificial intelligence is an important product of the rapid development of computer technology today. This study intends to propose an intelligent diagnosis and detection method for AR based on ensemble learning. Method: This study collectedAR cases and other 7 types of diseases with similar symptoms:Rhinosinusitis, Chronic rhinitis, upper respiratory tract infection etc.) and collected clinical data such as medical history, clinical symptoms, allergen detection and imaging. Multiple models are used to train the classifier for the same batch of data, and the final ensemble classifier is obtained by using the ensemble learning algorithm. 5 common machine learning classification algorithms were selected for comparative experiments, including Naive Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), Multilayer Perceptron (MLP), Deep Forest (GCForest), eXtreme Gradient boosting (XGBoost). In order to evaluate the prediction results of AR samples, parameters such as Precision, Sensitivity, Specificity, G-Mean, F1-Score, and AUC under the ROC curve are jointly used as prediction evaluation indicators. Results: 7 classification models are used for comparison, covering probability model, tree model, linear model, ensemble model and neural network models, and the comprehensive classification evaluation index is lower than the ensemble classification algorithms ARF-OOBEE and GCForest. Compared with other algorithms, the accuracy of G-Mean and AUC parameters is improved nearly 2%, and it has good comprehensive classification characteristics for massive large data and unbalanced samples. Conclusion: The ensemble learning ARF-OOBEE model has good generalization performance and comprehensive classification ability to be used for diagnosis of AR.

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“…The standard Brownian motion [31] is a stochastic process for which the step is given by a probability function defined by the zero mean (µ = 0) and unit variance (σ 2 = 1) of the normal (Gaussian) distribution. The control density function of the motion at point x is as follows:…”

Section: Exploratory Phase Of High-speed Ratiomentioning

confidence: 99%

“…To utilize the minimum number of trees for classification, Paul et al [30] proposed an improved random forest classifier based on the number of important and unimportant features, which iteratively removes some unimportant features. To improve the protein structure prediction performance, Kalaiselvi et al [31] introduced an improved random forest classification (WPC-IRFC) technique based on weighted Pearson correlation, which has higher accuracy and shorter time. In response to the fact that SVMs do not adequately consider the distinction between numerical and nominal attributes, Peng et al [32] proposed a novel SVM algorithm for heterogeneous data learning, which embeds nominal attributes into the real space by minimizing the estimated generalization error.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Strategy Marine Predator Algorithm and Its Application in Joint Regularization Semi-Supervised ELM

Yang

Xia

et al. 2021

Mathematics

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A novel semi-supervised learning method is proposed to better utilize labeled and unlabeled samples to improve classification performance. However, there is exists the limitation that Laplace regularization in a semi-supervised extreme learning machine (SSELM) tends to lead to poor generalization ability and it ignores the role of labeled information. To solve the above problems, a Joint Regularized Semi-Supervised Extreme Learning Machine (JRSSELM) is proposed, which uses Hessian regularization instead of Laplace regularization and adds supervised information regularization. In order to solve the problem of slow convergence speed and the easy to fall into local optimum of marine predator algorithm (MPA), a multi-strategy marine predator algorithm (MSMPA) is proposed, which first uses a chaotic opposition learning strategy to generate high-quality initial population, then uses adaptive inertia weights and adaptive step control factor to improve the exploration, utilization, and convergence speed, and then uses neighborhood dimensional learning strategy to maintain population diversity. The parameters in JRSSELM are then optimized using MSMPA. The MSMPA-JRSSELM is applied to logging oil formation identification. The experimental results show that MSMPA shows obvious superiority and strong competitiveness in terms of convergence accuracy and convergence speed. Also, the classification performance of MSMPA-JRSSELM is better than other classification methods, and the practical application is remarkable.

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An efficient Pearson correlation based improved random forest classification for protein structure prediction techniques

Cited by 30 publications

References 17 publications

Machine Learning Tools for Long-Term Type 2 Diabetes Risk Prediction

Machine Learning Tools for Long-Term Type 2 Diabetes Risk Prediction

Artificial Intelligence Applications in allergic rhinitis diagnosis: Focus on Ensemble Learning

A Multi-Strategy Marine Predator Algorithm and Its Application in Joint Regularization Semi-Supervised ELM

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