On some aspects of minimum redundancy maximum relevance feature selection

Bugata, Peter; Drotár, Peter

doi:10.1007/s11432-019-2633-y

Cited by 50 publications

(20 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides this, another two kinds of feature-selection methods, F-score ( Polat and Guenes 2009 ) and mRMR ( Li et al, 2017 ; Bugata and Drotar 2020 ), were also used to select the optimal feature subsets. The cross-validation AUROCs of the models based on the top n features selected by these two methods are also plotted in Figure 2 .…”

Section: Resultsmentioning

confidence: 99%

m5Cpred-XS: A New Method for Predicting RNA m5C Sites Based on XGBoost and SHAP

et al. 2022

View full text Add to dashboard Cite

As one of the most important post-transcriptional modifications of RNA, 5-cytosine-methylation (m5C) is reported to closely relate to many chemical reactions and biological functions in cells. Recently, several computational methods have been proposed for identifying m5C sites. However, the accuracy and efficiency are still not satisfactory. In this study, we proposed a new method, m5Cpred-XS, for predicting m5C sites of H. sapiens, M. musculus, and A. thaliana. First, the powerful SHAP method was used to select the optimal feature subset from seven different kinds of sequence-based features. Second, different machine learning algorithms were used to train the models. The results of five-fold cross-validation indicate that the model based on XGBoost achieved the highest prediction accuracy. Finally, our model was compared with other state-of-the-art models, which indicates that m5Cpred-XS is superior to other methods. Moreover, we deployed the model on a web server that can be accessed through http://m5cpred-xs.zhulab.org.cn/, and m5Cpred-XS is expected to be a useful tool for studying m5C sites.

show abstract

Section: Resultsmentioning

confidence: 99%

m5Cpred-XS: A New Method for Predicting RNA m5C Sites Based on XGBoost and SHAP

et al. 2022

View full text Add to dashboard Cite

show abstract

“…As shown in Figure 4 , there are 21,440 features in total, including 19,136 from the common feature extraction network and 2,304 from three domain-specific feature extraction subnetworks. To reduce the feature redundancy and improve the running speed of the classification model, the maximum relevance and minimum redundancy (mRMR) algorithm ( Bugata and Drotar, 2020 ) is used to select the top 10% features with higher relevance to the label for classification.…”

Section: Methodsmentioning

confidence: 99%

Classification of tumor from computed tomography images: A brain-inspired multisource transfer learning under probability distribution adaptation

Liu

Cui²

2022

Front. Hum. Neurosci.

View full text Add to dashboard Cite

Preoperative diagnosis of gastric cancer and primary gastric lymphoma is challenging and has important clinical significance. Inspired by the inductive reasoning learning of the human brain, transfer learning can improve diagnosis performance of target task by utilizing the knowledge learned from the other domains (source domain). However, most studies focus on single-source transfer learning and may lead to model performance degradation when a large domain shift exists between the single-source domain and target domain. By simulating the multi-modal information learning and transfer mechanism of human brain, this study designed a multisource transfer learning feature extraction and classification framework, which can enhance the prediction performance of the target model by using multisource medical data (domain). First, this manuscript designs a feature extraction network that takes the maximum mean difference based on the Wasserstein distance as an adaptive measure of probability distribution and extracts the domain-specific invariant representations between source and target domain data. Then, aiming at the random generation of parameters bringing uncertainties to prediction accuracy and generalization ability of extreme learning machine network, the 1-norm regularization is used to implement sparse constraints of the output weight matrix and improve the robustness of the model. Finally, some experiments are carried out on the data of two medical centers. The experimental results show that the area under curves (AUCs) of the method are 0.958 and 0.929 in the two validation cohorts, respectively. The method in this manuscript can provide doctors with a better diagnostic reference, which has certain practical significance.

show abstract

“…The PFA is similar to the class of methods minimizing redundancy maximizing relevance (mRMR) [31,12,34,45,3,26]. The basic concept of mRMR methods is to find a selection of features that is most relevant to an output function, meaning that the information of the feature is well suited for a prediction of the output variable, and at the same time that the features within the selection are minimally redundant to each other feature in the selection, meaning that the information to predict a feature from a different feature in the selection is small.…”

Section: Related Methodsmentioning

confidence: 99%

A principle feature analysis

Breitenbach,

Rasbach,

Liang

et al. 2021

Preprint

View full text Add to dashboard Cite

A key task of data science is to identify relevant features linked to certain output variables that are supposed to be modeled or predicted. To obtain a small but meaningful model, it is important to find stochastically independent variables capturing all the information necessary to model or predict the output variables sufficiently. Therefore, we introduce in this work a framework to detect linear and non-linear dependencies between different features. As we will show, features that are actually functions of other features do not represent further information. Consequently, a model reduction neglecting such features conserves the relevant information, reduces noise and thus improves the quality of the model. Furthermore, a smaller model makes it easier to adopt a model of a given system. In addition, the approach structures dependencies within all the considered features. This provides advantages for classical modeling starting from regression ranging to differential equations and for machine learning.To show the generality and applicability of the presented framework 2154 features of a data center are measured and a model for classification for faulty and non-faulty states of the data center is set up. This number of features is automatically reduced by the framework to 161 features. The prediction accuracy for the reduced model even improves compared to the model trained on the total number of features. A second example is the analysis of a gene expression data set where from 9513 genes 9 genes are extracted from whose expression levels two cell clusters of macrophages can be distinguished.

show abstract

On some aspects of minimum redundancy maximum relevance feature selection

Cited by 50 publications

References 26 publications

m5Cpred-XS: A New Method for Predicting RNA m5C Sites Based on XGBoost and SHAP

m5Cpred-XS: A New Method for Predicting RNA m5C Sites Based on XGBoost and SHAP

Classification of tumor from computed tomography images: A brain-inspired multisource transfer learning under probability distribution adaptation

A principle feature analysis

Contact Info

Product

Resources

About