Feature selection becomes prominent, especially in the data sets with many variables and features. It will eliminate unimportant variables and improve the accuracy as well as the performance of classification. Random Forest has emerged as a quite useful algorithm that can handle the feature selection issue even with a higher number of variables. In this paper, we use three popular datasets with a higher number of variables (Bank Marketing, Car Evaluation Database, Human Activity Recognition Using Smartphones) to conduct the experiment. There are four main reasons why feature selection is essential. First, to simplify the model by reducing the number of parameters, next to decrease the training time, to reduce overfilling by enhancing generalization, and to avoid the curse of dimensionality. Besides, we evaluate and compare each accuracy and performance of the classification model, such as Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA). The highest accuracy of the model is the best classifier. Practically, this paper adopts Random Forest to select the important feature in classification. Our experiments clearly show the comparative study of the RF algorithm from different perspectives. Furthermore, we compare the result of the dataset with and without essential features selection by RF methods varImp(), Boruta, and Recursive Feature Elimination (RFE) to get the best percentage accuracy and kappa. Experimental results demonstrate that Random Forest achieves a better performance in all experiment groups.

/pdf/selecting-critical-features-for-data-classification-based-on-vlfz2eyf1v.pdf

Selecting critical features for data classification based on machine learning methods

A broad review on class imbalance learning techniques

FW-SMOTE: A feature-weighted oversampling approach for imbalanced classification

Due to the development of biomedical equipment and healthcare level, especially in the Intensive Care Unit (ICU), a considerable amount of data has been collected for analysis. Mortality prediction in the ICUs is considered as one of the most important topics in the healthcare data analysis section. A precise prediction of the mortality risk for patients in ICU could provide us with valuable information about patients’ lives and reduce costs at the earliest possible stage. This paper aims to introduce a new hybrid predictive model using the Genetic Algorithm as a feature selection method and a new ensemble classifier based on the combination of Stacking and Boosting ensemble methods to create an early mortality prediction model on a highly imbalanced dataset. The SVM-SMOTE method is used to solve the imbalanced data problem. This paper compares the new model with various machine learning models to validate the efficiency of the introduced model. The achieved results using the shuffle 5-fold cross-validation and random hold-out methods indicate that the new hybrid model has the best performance among other classifiers. Additionally, the Friedman test is applied as a statistical significance test to examine the differences between classifiers. The results of the statistical analysis prove that the proposed model is more effective than other classifiers. Furthermore, the proposed model is compared to APACHE and SAPS scoring systems and is benchmarked against state-of-the-art predictive models applied to the MIMIC dataset for experimental validation and achieved promising results as it outperformed the state-of-the-art models.

/pdf/a-new-hybrid-predictive-model-to-predict-the-early-mortality-159xr5ty4x.pdf

A New Hybrid Predictive Model to Predict the Early Mortality Risk in Intensive Care Units on a Highly Imbalanced Dataset

Utilizing neuroimaging and machine learning (ML) to differentiate schizophrenia (SZ) patients from normal controls (NCs) and for detecting abnormal brain regions in schizophrenia has several benefits and can provide a reference for the clinical diagnosis of schizophrenia. In this study, structural magnetic resonance images (sMRIs) from SZ patients and NCs were used for discriminative analysis. This study proposed an ML framework based on coarse-to-fine feature selection. The proposed framework used two-sample t-tests to extract the differences between groups first, then further eliminated the nonrelevant and redundant features with recursive feature elimination (RFE), and finally utilized the support vector machine (SVM) to learn the decision models with selected gray matter (GM) and white matter (WM) features. Previous studies have tended to report differences at the group level instead of at the individual level and cannot be widely applied. The method proposed in this study extends the diagnosis to the individual level and has a higher recognition rate than previous methods. The experimental results of this study demonstrate that the proposed framework distinguishes SZ patients from NCs, with the highest classification accuracy reaching over 85%. The identified biomarkers are also consistent with previous literature findings. As a universal method, the proposed framework can be extended to diagnose other diseases.

/pdf/detecting-abnormal-brain-regions-in-schizophrenia-using-27w6u4mn5d.pdf

Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning

High-dimensional problems result in bad classification results because some combinations of features have an adverse effect on classification; while class-imbalanced problems make the classifier to concern the majority class more but the minority less, because the number of samples of majority class is more than minority class The problem of both high-dimensional and class-imbalanced classification is found in many fields such as bioinformatics, healthcare and so on Many researchers study either the high-dimensional problem or class-imbalanced problem and come up with a series of algorithms, but they ignore the above new problem, which indicates high-dimensional problems affect sampling process while class-imbalanced problems interfere feature selection Firstly, this paper analyses the new problem arising from the mutual influence of the two problems, and then introduces SVM and analyses its advantages in dealing high-dimensional problem and class-imbalanced problem Next, this paper proposes a new algorithm named BRFE-PBKS-SVM aimed at high-dimensional class-imbalanced datasets, which improves SVM-RFE by considering the class-imbalanced problem in the process of feature selection, and it also improves SMOTE so that the procedure of over-sampling could work in the Hilbert space with an adaptive over-sampling rate by PSO Finally, the experimental results show the performance of this algorithm

Research on classification method of high-dimensional class-imbalanced datasets based on SVM

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Research on classification method of high-dimensional class-imbalanced datasets based on SVM