Single Feature Extraction Method Research Articles

Breast cancer lesion segmentation based on co-learning feature fusion and Transformer

The PET/CT imaging technology combining positron emission tomography (PET) and computed tomography (CT) is the most advanced imaging examination method currently, and is mainly used for tumor screening, differential diagnosis of benign and malignant tumors, staging and grading. This paper proposes a method for breast cancer lesion segmentation based on PET/CT bimodal images, and designs a dual-path U-Net framework, which mainly includes three modules: encoder module, feature fusion module and decoder module. Among them, the encoder module uses traditional convolution for feature extraction of single mode image; The feature fusion module adopts collaborative learning feature fusion technology and uses Transformer to extract the global features of the fusion image; The decoder module mainly uses multi-layer perceptron to achieve lesion segmentation. This experiment uses actual clinical PET/CT data to evaluate the effectiveness of the algorithm. The experimental results show that the accuracy, recall and accuracy of breast cancer lesion segmentation are 95.67%, 97.58% and 96.16%, respectively, which are better than the baseline algorithm. Therefore, it proves the rationality of the single and bimodal feature extraction method combining convolution and Transformer in the experimental design of this article, and provides reference for feature extraction methods for tasks such as multimodal medical image segmentation or classification.

Network Traffic Classification Model Based on Spatio-Temporal Feature Extraction

The demand for encrypted communication is increasing with the continuous development of secure and trustworthy networks. In edge computing scenarios, the requirement for data processing security is becoming increasingly high. Therefore, the accurate identification of encrypted traffic has become a prerequisite to ensure edge intelligent device security. Currently, encrypted network traffic classification relies on single-feature extraction methods. These methods have simple feature extraction, making distinguishing encrypted network data flows and designing compelling manual features challenging. This leads to low accuracy in multi-classification tasks involving encrypted network traffic. This paper proposes a hybrid deep learning model for multi-classification tasks to address this issue based on the synergy of dilated convolution and gating unit mechanisms. The model comprises a Gated Dilated Convolution (GDC) module and a CA-LSTM module. The GDC module completes the spatial feature extraction of encrypted network traffic through dilated convolution and gating unit mechanisms. In contrast, the CA-LSTM module focuses on extracting temporal network traffic features. By employing a collaborative approach to extract spatio-temporal features, the model ensures feature extraction diversity, guarantees robustness, and effectively enhances the feature extraction rate. We evaluate our multi-classification model using the ISCX VPN-nonVPN public dataset. Experimental results show that the proposed method achieves an accuracy rate of over 95% and a recall rate of over 90%, significantly outperforming existing methods.

Integration of Colbp and Viola Jones Feature Extraction Methods in Gender Classification Based on Facial Image

Nowadays face recognition still being a hot topics to be discussed especially it’s utility for gender classification. Gender classification is an easy task for human but it’s a challenging task for computers because it doesn’t have capability for recognizing human gender without feature extraction. There are still many researches about facial image feature extraction for gender classification. Geometry features and Texture Features are well perform features for gender classification. This paper will presents fusion of those feature in order to find an improvement for gender classifications task. Each features will be extracted using Viola Jones Algorithm and Compass Local Binary Pattern method. Both features will be combined using concatenated method in dataframe format. Viola Jones algorithm has an issues when detecting each facial regions so it causes outliers in each geometry features. The proposed method will be evaluated on color FERET dataset that contains facial images. Classification task will be done using Random Forest and Backpropagation. The result is fusion features present an improvement in gender classification using Backpropagation with 87% accuracy. It prove that proposed method perform better than single feature extraction method.

A new approach based on a 1D + 2D convolutional neural network and evolving fuzzy system for the diagnosis of cardiovascular disease from heart sound signals

Diagnostic methods for cardiovascular disease diagnosis based on heart sound classification have been widely investigated for their noninvasiveness, low-cost, and high efficiency. Most current researches either use manually designed functions or deep learning-based methods to extract features from heart sound signals, but the heart sound signals have highly nonstationary and complex data patterns due to environmental noise and the differences between different stethoscopes. Therefore, using a single feature extraction method does not result in a good feature representation. Moreover, deep learning-based feature extraction methods for heart sound signals usually only use 1D convolution or 2D convolution, which limits the capability of neural networks to extract discriminative features. In addition, many studies do not consider the redundancy of features and the interpretability of decisions, which affects the performance and efficiency of the models. To solve the above problems, this paper first proposes a new convolutional neural network named the 1D + 2D convolutional neural network (1D + 2D-CNN) as a deep learning feature extractor, which combines 1D convolution and 2D convolution. The 1D + 2D-CNN contains two branches, and the feature maps obtained from the two branches are concatenated according to the channel. Then, a 10-layer convolutional network with an attention mechanism is introduced to enhance the feature extraction capability of the network. Second, the advantages and disadvantages when combining deep learning features with manual features in different scenarios are explored. In addition, the mean and variance of each dimensional feature of the dataset are calculated by class and feature selection is achieved by evaluating the importance of each dimension of the feature through a simple statistical formula. Finally, an evolving fuzzy system is used to classify the heart sound signals, as it can provide interpretability for decision-making. For the experimental part, the 2016 PhysioNet/CinC Challenge dataset (PCCD) and our collected publicly available pediatric heart sound dataset (PHSD) are used to evaluate the performance of the model by 10-fold cross-validation. The model in this paper achieves accuracies of 96.3 % and 99.1 % on these two datasets respectively, demonstrating its capability to reach the state-of-the-art level. We also open the code of our algorithms. Diagnostic methods for cardiovascular disease diagnosis based on heart sound classification have been widely investigated for their noninvasiveness, low-cost, and high efficiency. Most current researches either use manually designed functions or deep learning-based methods to extract features from heart sound signals, but the heart sound signals have highly nonstationary and complex data patterns due to environmental noise and the differences between different stethoscopes. Therefore, using a single feature extraction method does not result in a good feature representation. Moreover, deep learning-based feature extraction methods for heart sound signals usually only use 1D convolution or 2D convolution, which limits the capability of neural networks to extract discriminative features. In addition, many studies do not consider the redundancy of features and the interpretability of decisions, which affects the performance and efficiency of the models. To solve the above problems, this paper first proposes a new convolutional neural network named the 1D + 2D convolutional neural network (1D + 2D-CNN) as a deep learning feature extractor, which combines 1D convolution and 2D convolution. The 1D + 2D-CNN contains two branches, and the feature maps obtained from the two branches are concatenated according to the channel. Then, a 10-layer convolutional network with an attention mechanism is introduced to enhance the feature extraction capability of the network. Second, the advantages and disadvantages when combining deep learning features with manual features in different scenarios are explored. In addition, the mean and variance of each dimensional feature of the dataset are calculated by class and feature selection is achieved by evaluating the importance of each dimension of the feature through a simple statistical formula. Finally, an evolving fuzzy system is used to classify the heart sound signals, as it can provide interpretability for decision-making. For the experimental part, the 2016 PhysioNet/CinC Challenge dataset (PCCD) and our collected publicly available pediatric heart sound dataset (PHSD) are used to evaluate the performance of the model by 10-fold cross-validation. The model in this paper achieves accuracies of 96.3 % and 99.1 % on these two datasets respectively, demonstrating its capability to reach the state-of-the-art level. We also open the code of our algorithms.

TCI-UNet: transformer-CNN interactive module for medical image segmentation.

Medical image segmentation is a crucial step in developing medical systems, especially for assisting doctors in diagnosing and treating diseases. Currently, UNet has become the preferred network for most medical image segmentation tasks and has achieved tremendous success. However, due to the limitations of convolutional operation mechanisms, its ability to model long-range dependencies between features is limited. With the success of transformers in the computer vision (CV) field, many excellent models that combine transformers with UNet have emerged, but most of them have fixed receptive fields and a single feature extraction method. To address this issue, we propose a transformer-CNN interactive (TCI) feature extraction module and use it to construct TCI-UNet. Specifically, we improve the self-attention mechanism in transformers to enhance the guiding ability of attention maps for computational resource allocation. It can strengthen the network's ability to capture global contextual information from feature maps. Additionally, we introduce local multi-scale information to supplement feature information, allowing the network to focus on important local information while modeling global contextual information. This improves the network's capability to extract feature map information and facilitates effective interaction between global and local information within the transformer, enhancing the representational power of transformers. We conducted a large number of experiments on the LiTS-2017 and ISIC-2018 datasets to verify the effectiveness of our proposed method, with DCIE values of 93.81% and 88.22%, respectively. Through ablation experiments, we proved the effectiveness of the TCI module, and in comparison with other state-of-the-art (SOTA) networks, we demonstrated the superiority of TCI-UNet in accuracy and generalization.

Motor intent recognition of multi-feature fusion EEG signals by UMAP algorithm.

The key to the analysis of electroencephalogram (EEG) signals lies in the extraction of effective features from the raw EEG signals, which can then be utilized to augment the classification accuracy of motor imagery (MI) applications in brain-computer interface (BCI). It can be argued that the utilization of features from multiple domains can be a more effective approach to feature extraction for MI pattern classification, as it can provide a more comprehensive set of information that the traditional single feature extraction method may not be able to capture. In this paper, a multi-feature fusion algorithm based on uniform manifold approximate and projection (UMAP) is proposed for motor imagery EEG signals. The brain functional network and common spatial pattern (CSP) are initially extracted as features. Subsequently, UMAP is utilized to fuse the extracted multi-domain features to generate low-dimensional features with improved discriminative capability. Finally, the k-nearest neighbor (KNN) classifier is applied in a lower dimensional space. The proposed method is evaluated using left-right hand EEG signals, and achieved the average accuracy of over 92%. The results indicate that, compared with single-domain-based feature extraction methods, multi-feature fusion EEG signal classification based on the UMAP algorithm yields superior classification and visualization performance. Feature extraction and fusion based on UMAP algorithm of left-right hand motor imagery.

SOH estimation and RUL prediction of lithium batteries based on multidomain feature fusion and CatBoost model

AbstractIn this paper, a lithium‐ion battery State of Health (SOH) estimation algorithm is proposed based on the fusion of multidomain features and the application of a CatBoost model. The aim is to address the issue of low prediction accuracy in SOH caused by the utilization of single‐feature extraction techniques. The algorithm encompasses the extraction of various features from the original charge–discharge data, including time‐domain, frequency‐domain, entropy, and time‐series features. Following the evaluation of feature importance, a feature selection process is conducted to eliminate redundant features that provide a limited contribution to the predictive results. Subsequently, a multiple‐set discriminative correlation analysis is employed to integrate high‐dimensional features. To attain accurate predictions, the CatBoost model is further optimized through the utilization of a sparrow search algorithm. Experimental results demonstrate that the proposed algorithm achieves accurate SOH estimations within individual batteries, as evidenced by mean square error values consistently below 4e−4 and goodness‐of‐fit values exceeding or equal to 0.98. Additionally, the algorithm exhibits reliable prediction capabilities across different batteries operating under the same charge/discharge strategy. Comparative analysis indicates that the adoption of the multidomain feature fusion approach yields improved prediction accuracy in contrast to the utilization of a single feature extraction method.

Computational model for breast cancer diagnosis using HFSE framework

Mammography is one of the imaging modalities used in diagnosing breast cancer at an earlier stage. Misdiagnosis leads to risks for the patients. Better feature extraction and selection techniques can reduce misdiagnoses as they are essential in better-performing classifiers. The proposed approach in this paper introduces a novel Hybrid Feature Extraction and Hybrid Feature Selection (HFSE) framework. It uses mammograms from Digital Database for Screening Mammography (DDSM) datasets to classify mammograms into benign and malignant images. This paper presents a novel hybrid feature extraction approach from the Gray level co-occurrence matrix (GLCM), Linear local binary pattern (LBP), Gabor, and Tamura. Combinedly, it extracts 104 features to train the advanced classifiers such as Logistic Regression, Linear Perceptron, Support Vector Machine, Decision Tree, and Artificial Neural Network. The proposed Hybrid feature extraction method vigorously compares combinations of existing single feature extraction methods. The paper also presents a novel hybrid feature selection approach to choose a subset of the most relevant feature. It compares the Intrinsic feature with the proposed hybrid feature selection method. Hyperparameter tuning and Pipeline optimization techniques applied to the classifiers improve their performance metrics. The experimental results show that the proposed framework performs better using Hybrid feature extraction and feature selection on Artificial Neural Networks. This paper makes a comparative analysis of the related works. It outperforms by achieving a classifier accuracy of 94.57%, specificity of 91.80%, the sensitivity of 95.59 %, and an F1-score of 94.89% on Artificial Neural Networks.

An Indoor Visual Positioning Method with 3D Coordinates Using Built-In Smartphone Sensors Based on Epipolar Geometry.

In the process of determining positioning point by constructing geometric relations on the basis of the positions and poses obtained from multiple pairs of epipolar geometry, the direction vectors will not converge due to the existence of mixed errors. The existing methods to calculate the coordinates of undetermined points directly map the three-dimensional direction vector to the two-dimensional plane and take the intersection points that may be at infinity as the positioning result. To end this, an indoor visual positioning method with three-dimensional coordinates using built-in smartphone sensors based on epipolar geometry is proposed, which transforms the positioning problem into solving the distance from one point to multiple lines in space. It combines the location information obtained by the accelerometer and magnetometer with visual computing to obtain more accurate coordinates. Experimental results show that this positioning method is not limited to a single feature extraction method when the source range of image retrieval results is poor. It can also achieve relatively stable localization results in different poses. Furthermore, 90% of the positioning errors are lower than 0.58 m, and the average positioning error is less than 0.3 m, meeting the accuracy requirements for user localization in practical applications at a low cost.

Anomaly traffic detection based on feature fluctuation for secure industrial internet of things.

The detection of anomaly traffic in internet of things (IoT) is mainly based on the original binary data at the traffic packet level and the structured data at the session flow level. This kind of dataset has a single feature extraction method and relies on prior manual knowledge. It is easy to lose critical information during data processing, which reduces the validity and robustness of the dataset. In this paper, we first construct a new anomaly traffic dataset based on the traffic packet and session flow data in the Iot-23 dataset. Second, we propose a feature extraction method based on feature fluctuation. Our proposed method can effectively solve the disadvantage that the data collected in different scenarios have different characteristics, which leads to the feature containing less information. Compared with the traditional anomaly traffic detection model, experiments show that our proposed method based on feature fluctuation has stronger robustness, can improve the accuracy of anomaly traffic detection and the generalization ability of the traditional model, and is more conducive to the detection of anomalous traffic in IoT.

Convolution neural networks with hybrid feature extraction methods for classification of voice sound signals

The convolutional neural networks (CNNs) lead in the domain of Sound Recognition due to its flexibility and ability with different adjusting parameters. The recognition of spoken English Alphabets by different people with deep learning techniques attracted the research community. In this paper, we are exploring the use of convolutional neural network (CNN), a deep learner that can automatically learn features directly from the dataset while training for the classification of sounds signals of English alphabets. In this proposed work, we consider two CNN architectures. In first architecture, we propose MFCC based features for pretrained two convolutional layer CNN architecture. In the second architecture, we propose a hybrid feature extraction method to train a block-based CNN architecture. The proposed systems consist of two components namely hybrid feature extraction and CNN classifier. The five auditory features log-Mel spectrogram (LM), MFCC, chroma, spectral contrast and Tonnetz features are extracted and then LM & MFCC are combined as one feature set. LM, MFCC, and CST features are aggregated as another for training to the proposed two CNNs, respectively. The different sound samples of English alphabets are collected from different people of different age groups. The feature sets collected from the hybrid feature extraction methods are presented to both the proposed CNNs and the experimental results are collected. The experimental results indicate that the taxonomic accuracy of the proposed architectures can surpass the existing methods of CNNs with single feature extraction methods. The proposed second architecture performs more effectively over the proposed first CNN architecture.

An efficient feature selection method for arabic and english speech emotion recognition using Grey Wolf Optimizer

Nowadays, analyzing and interpreting emotions through human speech communication have drawn a great attention in the field of human-computer interaction. Therefore, many speech recognition systems have been suggested to recognize the emotional states of the speaker utilizing the speech recordings of their spoken utterances. Feature extraction is an important step in building an emotion recognition system in which it is used to extract emotional features from speech data. However, not all extracted features are relevant to classify the emotion states of the speaker. The existence of irrelevant and redundant features generates unmeaningful patterns that lead to inaccurate and undesirable emotion classification performance. Therefore, this study proposes an intelligent feature selection method based on a novel bio-inspired optimization algorithm that mimics the hunting mechanism of wolves in the nature, called Grey Wolf Optimizer (GWO) and K-nearest neighbor (KNN) classifier, to find the most relevant subset of features to enhance the classification performance of an emotion recognition systems. The proposed method is called GWO-KNN. Emotion classification is performed on three distinct databases including Arabic Emirati-accented speech database, Ryerson Audio-Visual Database of Emotional Speech and Song dataset (RAVDESS), and Surrey Audio-Visual Expressed Emotion dataset (SAVEE). A combined or single feature extraction method is applied to extract the features from each dataset. The proposed method provides better classification performance for speech emotion recognition system compared to classical methods such as bat algorithm (BAT), cuckoo search (CS), White Shark Optimizer (WSH), and arithmetic optimization algorithm (AOA). Our proposed method also surpasses several state-of-the-art recent approaches that use the same datasets.

Research on PM2.5 Pollution Prediction Method in Hefei City Based on CNN-LSTM Hybrid Model

The air quality data has the characteristics of large scale and high time sequence. In the deep learning model, CNN has good feature extraction and expression ability through convolution operation. LSTM has strong time series data processing ability, which effectively solves the long-term dependence problem in the time series data that CNN cannot process. However, a single feature extraction or time series processing method will affect the accuracy of the model. This paper proposes a PM2.5 pollution prediction method based on the CNN-LSTM hybrid model, which can fuse the time series and nonlinear features of the data. By selecting the air quality and meteorological data of Hefei City in Anhui Province from 2018 to 2021 for prediction research, the experimental results show that the CNN-LSTM hybrid model is better than the LSTM model in terms of RMSE, MAE, and R2 evaluation indexes, which reflects higher prediction accuracy. It can not only be used for real-time prediction of PM2.5 pollution, but also for the prediction of other pollutants in the atmosphere. It also provides a reference for the application of CNN-LSTM model in solving other time series big data.

Multi-dimensional shared representation learning with graph fusion network for Session-based Recommendation

The Session-based Recommendation (SBR) system aims to forecast anonymous users’ short-term decisions. Many prior research have demonstrated that using Graph Neural Networks (GNN) and Recurrent Neural Networks (RNN) to solve SBR tasks can lead to excellent results. However, the existing SBR models only use single feature extraction method to represent an item in the recommendation process. These models either use the GNN methods which ignore the sequence location features in the training process or use RNN methods which cannot obtain the weight features of every item in the sequence. All of them fail to extract features of different dimensions at the same time, which results in low-quality item representations. To tackle the aforementioned issue, this research introduces a novel method called Multi-dimensional Shared Representation Learning (MSR). (i) To get multi-dimensional features, we use the multi-dimensional feature extraction in a double-flow way based on transformer layers and graph attention network (GAT) layers. (ii) Through the MSR module, we train the joint representation of items in the session by using multiple GNNs, then merge the multi-dimensional features by using an attention mechanism. A variety of experiments have been carried out and the simulation results demonstrate the designed strategy largely surpasses the state-of-the-art proposal in recommendation accuracy.

IUP-BERT: Identification of Umami Peptides Based on BERT Features.

Umami is an important widely-used taste component of food seasoning. Umami peptides are specific structural peptides endowing foods with a favorable umami taste. Laboratory approaches used to identify umami peptides are time-consuming and labor-intensive, which are not feasible for rapid screening. Here, we developed a novel peptide sequence-based umami peptide predictor, namely iUP-BERT, which was based on the deep learning pretrained neural network feature extraction method. After optimization, a single deep representation learning feature encoding method (BERT: bidirectional encoder representations from transformer) in conjugation with the synthetic minority over-sampling technique (SMOTE) and support vector machine (SVM) methods was adopted for model creation to generate predicted probabilistic scores of potential umami peptides. Further extensive empirical experiments on cross-validation and an independent test showed that iUP-BERT outperformed the existing methods with improvements, highlighting its effectiveness and robustness. Finally, an open-access iUP-BERT web server was built. To our knowledge, this is the first efficient sequence-based umami predictor created based on a single deep-learning pretrained neural network feature extraction method. By predicting umami peptides, iUP-BERT can help in further research to improve the palatability of dietary supplements in the future.

Feature extraction methods of ship-radiated noise: From single feature of multi-scale dispersion Lempel-Ziv complexity to mixed double features

Dispersion Lempel-Ziv complexity (DLZC) has been introduced into the field of underwater acoustic with great performance, but it only reflects complexity information from single-scale. To address this problem, we proposed multi-scale dispersion Lempel-Ziv complexity (MDLZC), which reflects the complexity of ship-radiated noise signals (SNSs) more comprehensively. Based on the proposed MDLZC, we put forward a single feature extraction method for SNSs, which denoted as 1-MDLZC; to enhance the recognition validity of 1-MDLZC, we came up with a double features extraction method based on MDLZC under two scale factors (SFs), which denoted as 2-MDLZC; to associate the complementarity of MDLZC and multi-scale dispersion entropy (MDE), we advanced mixed double features extraction method based on MDLZC and MDE under two SFs, which denoted as 1-MDLZC&1-MDE. The experimental results demonstrated that, 1-MDLZC has the highest recognition rate compared to single feature extraction method based on MDE and multi-scale Lempel-Ziv complexity (MLZC), which up to 97%; 2-MDLZC improves the recognition rate of 1-MDLZC by 2.75%; 1-MDLZC&1-MDE makes the recognition rate achieve 100% in many cases under double features.

Nuclear cataract classification in anterior segment OCT based on clinical global–local features

Nuclear cataract (NC) is a priority ocular disease of blindness and vision impairment globally. Early intervention and cataract surgery can improve the vision and life quality of NC patients. Anterior segment coherence tomography (AS-OCT) imaging is a non-invasive way to capture the NC opacity objectively and quantitatively. Recent clinical research has shown that there exists a strong opacity correlation relationship between NC severity levels and the mean density on AS-OCT images. In this paper, we present an effective NC classification framework on AS-OCT images, based on feature extraction and feature importance analysis. Motivated by previous clinical knowledge, our method extracts the clinical global–local features, and then applies Pearson’s correlation coefficient and recursive feature elimination methods to analyze the feature importance. Finally, an ensemble logistic regression is employed to distinguish NC, which considers different optimization methods’ characteristics. A dataset with 11,442 AS-OCT images is collected to evaluate the method. The results show that the proposed method achieves 86.96% accuracy and 88.70% macro-sensitivity, respectively. The performance comparison analysis also demonstrates that the global–local feature extraction method improves about 2% accuracy than the single region-based feature extraction method.

Particle Swarm Optimization Fractional Slope Entropy: A New Time Series Complexity Indicator for Bearing Fault Diagnosis

Slope entropy (SlEn) is a time series complexity indicator proposed in recent years, which has shown excellent performance in the fields of medical and hydroacoustics. In order to improve the ability of SlEn to distinguish different types of signals and solve the problem of two threshold parameters selection, a new time series complexity indicator on the basis of SlEn is proposed by introducing fractional calculus and combining particle swarm optimization (PSO), named PSO fractional SlEn (PSO-FrSlEn). Then we apply PSO-FrSlEn to the field of fault diagnosis and propose a single feature extraction method and a double feature extraction method for rolling bearing fault based on PSO-FrSlEn. The experimental results illustrated that only PSO-FrSlEn can classify 10 kinds of bearing signals with 100% classification accuracy by using double features, which is at least 4% higher than the classification accuracies of the other four fractional entropies.

Feature Extraction Method of Epileptic EEG Signal based on Wavelet Packet and Improved Fuzzy Entropy

Epileptic eeg signal has obvious characteristic information, which can be used as an important basis to judge whether epileptic seizure occurs. Because of the low recognition rate of single feature extraction method, a method of eeg feature extraction based on wavelet packet transform and improved fuzzy entropy was proposed. In view of the characteristics of eeg signal with large noise and weak signal, the Wavelet packet Transform (WPT) is used to decompose the EEG signal with multi-resolution and make it into the signal with different characteristics. The original Fuzzy entropy (Fuzzy EN) algorithm was improved to improve its ability of reflecting the degree of irregularity and complexity of time series. Finally, the feature extraction of epileptic EEG signal was completed by combining the wavelet packet transform method.

Single Feature Extraction Method of Bearing Fault Signals Based on Slope Entropy

As an entropy representing the complexity of sequence, slope entropy (SloE) is applied to feature extraction of bearing signal for the first time. With the advantage of slope entropy in feature extraction, the effectiveness of bearing fault signal diagnosis can be verified. Five different kinds of entropy are selected to be comparative methods for experiments, and they are permutation entropy (PE), dispersion entropy (DE), a version of entropy adapted by PE, which is weighted permutation entropy (WPE), and two versions of entropy adapted by DE, which are fluctuating dispersion entropy (FDE) and reverse dispersion entropy (RDE). A method of extracting a single feature of bearing fault signals based on SloE is carried out. Firstly, the features of the bearing signals are extracted by the six kinds of entropy. Then, some relevant data are computed, and the identification ratios are calculated by the K-nearest neighbor (KNN) algorithm. The experimental result indicated that the identification ratio of SloE is the highest at 97.71% by comparing with the identification ratios of the other five kinds of entropy, which is higher by at least 13.54% than the others and 27.5% higher than the lowest one.