Performance Of Classification Methods Research Articles

Beta-informativeness-diffusion multilayer graph embedding for brain network analysis.

Brain network analysis provides essential insights into the diagnosis of brain disease. Integrating multiple neuroimaging modalities has been demonstrated to be more effective than using a single modality for brain network analysis. However, a majority of existing brain network analysis methods based on multiple modalities often overlook both complementary information and unique characteristics from various modalities. To tackle this issue, we propose the Beta-Informativeness-Diffusion Multilayer Graph Embedding (BID-MGE) method. The proposed method seamlessly integrates structural connectivity (SC) and functional connectivity (FC) to learn more comprehensive information for diagnosing neuropsychiatric disorders. Specifically, a novel beta distribution mapping function (beta mapping) is utilized to increase vital information and weaken insignificant connections. The refined information helps the diffusion process concentrate on crucial brain regions to capture more discriminative features. To maximize the preservation of the unique characteristics of each modality, we design an optimal scale multilayer brain network, the inter-layer connections of which depend on node informativeness. Then, a multilayer informativeness diffusion is proposed to capture complementary information and unique characteristics from various modalities and generate node representations by incorporating the features of each node with those of their connected nodes. Finally, the node representations are reconfigured using principal component analysis (PCA), and cosine distances are calculated with reference to multiple templates for statistical analysis and classification. We implement the proposed method for brain network analysis of neuropsychiatric disorders. The results indicate that our method effectively identifies crucial brain regions associated with diseases, providing valuable insights into the pathology of the disease, and surpasses other advanced methods in classification performance.

Self-Supervised Deep Multi-Level Representation Learning Fusion-Based Maximum Entropy Subspace Clustering for Hyperspectral Band Selection

As one of the most important techniques for hyperspectral image dimensionality reduction, band selection has received considerable attention, whereas self-representation subspace clustering-based band selection algorithms have received quite a lot of attention with good effect. However, many of them lack the self-supervision of representations and ignore the multi-level spectral–spatial information of HSI and the connectivity of subspaces. To this end, this paper proposes a novel self-supervised multi-level representation learning fusion-based maximum entropy subspace clustering (MLRLFMESC) method for hyperspectral band selection. Firstly, to learn multi-level spectral–spatial information, self-representation subspace clustering is embedded between the encoder layers of the deep-stacked convolutional autoencoder and its corresponding decoder layers, respectively, as multiple fully connected layers to achieve multi-level representation learning (MLRL). A new auxiliary task is constructed for multi-level representation learning and multi-level self-supervised training to improve its capability of representation. Then, a fusion model is designed to fuse the multi-level spectral–spatial information to obtain a more distinctive coefficient matrix for self-expression, where the maximum entropy regularization (MER) method is employed to promote connectivity and the uniform dense distribution of band elements in each subspace. Finally, subspace clustering is conducted to obtain the final band subset. Experiments have been conducted on three hyperspectral datasets, and the corresponding results show that the proposed MLRLFMESC algorithm significantly outperforms several other band selection methods in classification performance.

Penerapan Metode Support Vector Machine (SVM) dalam Klasifikasi Penderita Diabetes Mellitus

Diabetes Mellitus (DM) is a chronic disease characterized by the body's inability to metabolize carbohydrates, fats, and proteins, resulting in increased blood sugar (hyperglycemia) due to low insulin levels. Diabetes is due to a combination of heredity (genetics) and unhealthy lifestyles. Hemoglobin A1c is a blood test used to diagnose and manage diabetes patients when measuring blood sugar levels. This study aims to analyze predictive models for the classification of people with diabetes using R Shiny and evaluate the results of the support vector machine method's classification performance. There are many ways to diagnose diabetes, and the support vector machine is one of the machine learning algorithms used in this study's classification case (SVM). This study uses data from Diabetes 130-US Hospital For Years 1999-2008, which was sourced from the UCI Machine Learning Repository and consists of 34 variables and 84900 records, with dataset distribution and testing techniques using the 10-fold cross-validation method and three kernels in modeling using SVM, namely linear, Gaussian, and polynomial. The results obtained are a simple predictive model analysis system for classifying people with diabetes with shiny, making it easier for users to find out the prediction results and obtain the highest accuracy result, which is 82.76 percent of the gaussian kernel.

A novel twin-support vector machines method for binary classification to imbalanced data

Most existing classifiers are better at identifying majority classes instead of ignoring minority classes, which leads to classifier degradation. Therefore, it is a challenge for binary classification to imbalanced data, to address this, this paper proposes a novel twin-support vector machine method. The thought is that majority classes and minority classes are found by two support vector machines, respectively. The new kernel is derived to promote the learning ability of the two support vector machines. Results show that the proposed method wins over competing methods in classification performance and the ability to find minority classes. Those classifiers based-twin architectures have more advantages than those classifiers based-single architecture in classification ability. We demonstrate that the complexity of imbalanced data distribution has negative effects on classification results, whereas, the advanced classification results and the desired boundaries can be gained by optimizing the kernel.

IDA-Net: Inheritable Deformable Attention Network of structural MRI for Alzheimer’s Disease Diagnosis

To precisely diagnose neurological diseases, such as Alzheimer’s disease, clinicians need to observe the microstructural changes of local brain atrophy with the help of structural magnetic resonance image (sMRI). Some Convolutional Neural Networks (CNNs) have recently achieved excellent performance in auxiliary clinicians to provide the diagnosis suggestion. However, there still exist several challenges. Foremost, several researchers manually predefine some regions of interest (ROIs) as the input of the CNN-based networks, which impedes the model’s robustness and interpretability of clinical applications. Second, since the position relevance of pathological features interferes with the surrounding tissue regions in ROIs, it is hard for the current CNN-based networks to extract the microstructural changes of these ROIs precisely. To address the above challenges, we optimize the Transformer structure for Alzheimer’s Disease Diagnosis and propose an Inheritable Deformable Attention Network (IDA-Net). Specifically, the IDA-Net mainly comprises the 3D Deformable Self-Attention module and the Inheritable 3D Deformable Self-Attention module. The 3D Deformable Self-Attention module can automatically adjust the position and scale of the selected patches according to the structural changes in sMRI. Furthermore, the Inheritable 3D Deformable Self-Attention module can locate and output relatively important regions with discriminative features in sMRI, which can assist physicians in the clinical diagnosis. Our proposed IDA-Net method is evaluated on the sMRI of 2813 subjects from ADNI and AIBL datasets. The results show that our IDA-Net method behaves better than several state-of-the-art methods in classification performance and model generalization.

IMPLEMENTASI SUPPORT VECTOR MACHINE (SVM) UNTUK KLASIFIKASI PEDERITA DIABETES MELLITUS

Diabetes Mellitus (DM) is a chronic disease characterized by the body's inability to metabolize carbohydrates, fats, and proteins, resulting in increased blood sugar (hyperglycemia) due to low insulin levels. Diabetes is due to a combination of heredity (genetics) and unhealthy lifestyles. Hemoglobin A1c is a blood test used to diagnose and manage diabetes patients when measuring blood sugar levels. This study aims to analyze predictive models for the classification of people with diabetes using R Shiny and evaluate the results of the support vector machine method's classification performance. There are many ways to diagnose diabetes, and the support vector machine is one of the machine learning algorithms used in this study's classification case (SVM). This study uses data from Diabetes 130-US Hospital For Years 1999-2008, which was sourced from the UCI Machine Learning Repository and consists of 34 variables and 84900 records, with dataset distribution and testing techniques using the 10-fold cross-validation method and three kernels in modeling using SVM, namely linear, Gaussian, and polynomial. The results obtained are a simple predictive model analysis system for classifying people with diabetes with shiny, making it easier for users to find out the prediction results and obtain the highest accuracy result, which is 82.76 percent of the gaussian kernel. Keywords: diabetes mellitus; HbA1c; classification; support vector machine; 10-fold cross validation.

A Novel and Effective Brain Tumor Classification Model Using Deep Feature Fusion and Famous Machine Learning Classifiers.

Brain tumors are difficult to treat and cause substantial fatalities worldwide. Medical professionals visually analyze the images and mark out the tumor regions to identify brain tumors, which is time-consuming and prone to error. Researchers have proposed automated methods in recent years to detect brain tumors early. These approaches, however, encounter difficulties due to their low accuracy and large false-positive values. An efficient tumor identification and classification approach is required to extract robust features and perform accurate disease classification. This paper proposes a novel multiclass brain tumor classification method based on deep feature fusion. The MR images are preprocessed using min-max normalization, and then extensive data augmentation is applied to MR images to overcome the lack of data problem. The deep CNN features obtained from transfer learned architectures such as AlexNet, GoogLeNet, and ResNet18 are fused to build a single feature vector and then loaded into Support Vector Machine (SVM) and K-nearest neighbor (KNN) to predict the final output. The novel feature vector contains more information than the independent vectors, boosting the proposed method's classification performance. The proposed framework is trained and evaluated on 15,320 Magnetic Resonance Images (MRIs). The study shows that the fused feature vector performs better than the individual vectors. Moreover, the proposed technique performed better than the existing systems and achieved accuracy of 99.7%; hence, it can be used in clinical setup to classify brain tumors from MRIs.

Distance classifier ensemble based on intra-class and inter-class scatter

Distance classifier ensemble method based on Intra-class and Inter-class Scatter is proposed in this paper. By Bootstrap technology, the training samples are sampled repeatedly to generate several subsample set, define Intra-class and Inter-class Scatter matrix with subsample set, train subsample set with scatter matrix, generate individual classifier. In the classifier ensemble, the results are integrated with the relative majority voting method. Experiment is tested on UCI standard database, the experimental results show that the proposed ensemble method based on Intra-class and Inter-class Scatter for distance classifier is effective, and it is superior to other methods in classification performance.

CPMAN: Change Point Detection Approach in Time Series Based on the Prediction of Multi-stage Attention Networks

Time series change point detection can identify the locations of abrupt points in many dynamic processes. It can help us to find anomaly data in an early stage. At the same time, detecting change points for long, periodic, and multiple input series data has received a lot of attention recently, and is universally applicable in many fields including power, environment, finance, and medicine. However, the performance of classical methods typically scales poorly for such time series. In this paper, we propose CPMAN, a novel prediction-based change point detection approach via multi-stage attention networks. Our model consists of two key modules. First, in the time series prediction module, we employ the multi-stage attention-based networks and integrate the multi-series fusion mechanism. This module can adaptively extract features from the relevant input series and capture the long-term temporal dependencies. Secondly, in the change point detection module, we use the wavelet analysis-based algorithm to detect change points efficiently and identify the change points and outliers. Extensive experiments are conducted on various real-world datasets and synthetic datasets, proving the superiority and effectiveness of CPMAN. Our approach outperforms the state-of-the-art methods by up to 12.1% on the F1 Score.

Distance Classifier Ensemble Based on Intra-Class and Inter-Class Scatter

Distance classifier ensemble method based on Intra-class and Inter-class Scatter is proposed in this paper. By Bootstrap technology, the training samples are sampled repeatedly to generate several subsample set, define Intra-class and Inter-class Scatter matrix with subsample set, train subsample set with scatter matrix, generate individual classifier. In the classifier ensemble, the results are integrated with the relative majority voting method. Experiment is tested on UCI standard database, the experimental results show that the proposed ensemble method based on Intra-class and Inter-class Scatter for distance classifier is effective, and it is superior to other methods in classification performance.

A novel hybrid deep learning approach including combination of 1D power signals and 2D signal images for power quality disturbance classification

As a result of the widespread use of power electronic equipment and the increase in consumption, the importance of effective energy policies and the smart grid begins to increase. Nonlinear loads and other loads in electric power systems are considered as the main reason for power quality disturbance. Distortions in signal quality and shape due to power quality disturbance cause a decrease in total efficiency. The proposed hybrid convolutional neural network method consists of a 1D convolutional neural network structure and a 2D convolutional neural network structure. The features acquired by these two convolutional neural network architectures are classified using the fully connected layer, which is traditionally used as the classifier of convolutional neural network architectures. Power signals are processed using a 1D convolutional neural network in their original form. Then these signals are converted into images and processed using a 2D convolutional neural network. Then, feature vectors generated by 1D and 2D convolutional neural networks are combined. Finally, this combined vector is classified by a fully connected layer. The proposed method is well suited to the nature of signal processing. It is a novel approach that covers the steps of an expert examining a signal. The proposed framework is compared with other state-of-the-art power quality disturbance classification methods in the literature. While the proposed method's classification performance is relatively high compared to other methods, the computational complexity is almost the same.

A Multiple Remote Sensing Sensor Fusion System Using Choquet Fuzzy Integral and Modified Particle Swarm Optimization (FI-MPSO)

The simultaneous use of remote sensing sensors has been of great interest due to reasons such as the weaknesses of each sensor individually and the improvement in results by integrating multisensor information. In this context, decision fusion of multiremote sensing sensors is one of the hot research topics for improvement in land cover classification. This paper investigates a multisensor fusion system based on Choquet fuzzy integral and a modified particle swarm optimization for fusion of high-resolution visible RGB image and thermal infrared hyperspectral. Instead of using common optimization algorithms, a new version of PSO is used to reduce the running time and to increase the accuracy. After some processes and fuzzy classifications, a fuzzy integral–fuzzy measure fusion methodology which uses modified PSO to find the best subset of fuzzy measures is utilized to fuse the resulted decision profiles of soft decision-making system. Also, comparison between proposed method and other successful decision fusion techniques such as Dempsters shafer, AdaBoost and Bagging is investigated. Experiments are executed on high-resolution RGB image and thermal infrared hyperspectral data from Quebec of Canada. The results show that the suggested method obtains 92% of overall accuracy and outperforms the other decision fusion and optimization methods in classification performance.

Nuclei detection in hepatocellular carcinoma and dysplastic liver nodules in histopathology images using bootstrap regression.

Hepatocellular carcinoma (HCC) is the most common primary malignant neoplasm of the liver representing the fifth most common malignancy worldwide. This tumor is more common in men than women, with a ratio of 2.7:1. Unlike HCC, Dysplasia is the precancerous nature of liver nodules and is characterized by cellular and nuclear enlargement, nuclear pleomorphism, and multinucleation. Area based Adaptive Expectation Maximization (EM) uses texture, layout, and context features of cells, and grows clusters to obtain texton maps of nucleus. A discriminative model of nucleus and cytoplastic changes of tumor is built by incorporating texture, layout, and context information efficiently. A bootsrap regression model of nuclei and cytoplastic changes are built by incorporating the aforementioned features efficiently. Mean squared error, Peak Signal to Noise ratio and Dice similarity values are used to evaluate the method's classification performance. The proposed method provides high classification and segmentation accuracy of nucleus and extra nuclear content in HCC and dysplasia, which are exceedingly textured in histopathology images, when compared to Adaptive K means, EM method and the state-of-the-art method, Convolutional Neural Networks (CNN). As texton detection reduces the cluttered background of nuclei, the proposed method would be a convenient mechanism for the classification of nuclei and non-nuclear features. In conclusion, this system can detect more eligible cells of precancerous nature as well as malignant cells even in a cluttered background of nuclei.

Hybrid pooling for enhancement of generalization ability in deep convolutional neural networks

Convolutional neural networks (CNNs) have attracted considerable attention in many application fields for their great ability to deal with image recognition and object detection tasks. A pooling process is an important process in CNNs, which serves to decrease the dimensionality of processed data for reducing computational cost as well as for enhancing tolerance to translation and noise. Although standard pooling methods, such as the max pooling and the average pooling, are typically adopted in many studies, a newly devised pooling method could improve the generalization ability of CNNs. In this study, we propose a hybrid pooling method which stochastically chooses the max pooling or the average pooling in each pooling layer. A characteristic of the hybrid pooling is that the probability for choosing one of the two pooling methods can be controlled for each convolutional layer. In image classification tasks with benchmark datasets, we show that the hybrid pooling is effective for increasing the generalization ability of CNNs. Moreover, we demonstrate that the hybrid pooling combined with the dropout is competitive with other existing methods in classification performance.

Classifying the traffic state of urban expressways: A machine-learning approach

The classification of the urban traffic state and its application is an important part of intelligent transportation systems (ITS), which can not only help traffic managers grasp the traffic operation situation and analyze congestion, but also provide travelers with more traffic information and help them avoid congestion. Thus, an accurate traffic state classification method would be very practical for urban traffic management. The primary objective of this study is to classify the urban traffic state using a machine-learning method (i.e., the FCM clustering method, and the classification results can be determined from the corresponding clustering labels). In this approach, two parts are developed. First, a new classification indicator, i.e., the ample degree of road network is proposed, and it will make up a comprehensive classification indicator system with other parameters such as traffic flow, speed and occupancy. Then, the traditional fuzzy c-means (FCM) clustering approach is improved in two regards, i.e., the fuzzy membership function improvement and weighting processing of the samples, and these improvements can enhance the clustering performance. As a result, an improved machine-learning method (i.e., the improved FCM clustering approach) is developed and used to conduct the clustering analysis with real-world traffic flow data. Next, a case study of Shanghai is used to guide the study process, which consists of data processing, clustering analysis and method comparison. The other methods (e.g., the support vector machines (SVM) method, the decision tree method, the k-Nearest Neighbor (KNN) method and the traditional FCM clustering approach) are introduced to compare with the improved FCM clustering approach. The discussion shows the superiority of the proposed method (e.g., compared with the traditional FCM clustering approach, the objective function value of the improved method decreased by 31.11%, and cluster center error also show a descending trend), and it outperforms the other methods in classification performance (e.g., the overall classification accuracy of the improved FCM method increased by 10.10%, 5.45%, 30.92% and 35.66% in comparison with the traditional FCM method, SVM method, decision tree method and KNN method, respectively). Additionally, the NMI, ROC curve results also illustrated the superiority of the improved FCM method to other methods. These comaprison results suggest that the improved FCM clustering approach is feasible and the results can be well used in the advanced traffic management system, which may have the potential to serve as a reference for releasing accurate traffic state information and preventing traffic congestion and risk.

Rank-based pooling for deep convolutional neural networks

Pooling is a key mechanism in deep convolutional neural networks (CNNs) which helps to achieve translation invariance. Numerous studies, both empirically and theoretically, show that pooling consistently boosts the performance of the CNNs. The conventional pooling methods are operated on activation values. In this work, we alternatively propose rank-based pooling. It is derived from the observations that ranking list is invariant under changes of activation values in a pooling region, and thus rank-based pooling operation may achieve more robust performance. In addition, the reasonable usage of rank can avoid the scale problems encountered by value-based methods. The novel pooling mechanism can be regarded as an instance of weighted pooling where a weighted sum of activations is used to generate the pooling output. This pooling mechanism can also be realized as rank-based average pooling (RAP), rank-based weighted pooling (RWP) and rank-based stochastic pooling (RSP) according to different weighting strategies. As another major contribution, we present a novel criterion to analyze the discriminant ability of various pooling methods, which is heavily under-researched in machine learning and computer vision community. Experimental results on several image benchmarks show that rank-based pooling outperforms the existing pooling methods in classification performance. We further demonstrate better performance on CIFAR datasets by integrating RSP into Network-in-Network.

A New Shape Feature for Vehicle Classification in Thermal Video Sequences

Since thermal images represent only the temperature difference between objects and background and they have more blurred edges than color images, the segmented images from them have noisy object boundaries. Therefore, the well-known features developed for color images may not work well with thermal images. To overcome these limitations of thermal images, we propose a novel feature extraction method based on the target trait context (TTC). A robust keypoint detector is also proposed by analyzing the included angle moments of the object boundary points. At each keypoint, we define the boundary shape context and the normalized intensity context, which contain shape information and thermal distribution, respectively, of the object. These two contexts are combined to form a new feature set, the TTC. To validate our proposed feature extraction method, the keypoint repeatability test and the classification performance test were performed and compared with those from the previous methods. The experiment results show that the proposed feature works well for thermal video sequences and outperforms the previous methods in classification performance.

A Bayesian Classification Approach Using Class-Specific Features for Text Categorization

In this paper, we present a Bayesian classification approach for automatic text categorization using class-specific features. Unlike conventional text categorization approaches, our proposed method selects a specific feature subset for each class. To apply these class-specific features for classification, we follow Baggenstoss's PDF Projection Theorem (PPT) to reconstruct the PDFs in raw data space from the class-specific PDFs in low-dimensional feature subspace, and build a Bayesian classification rule. One noticeable significance of our approach is that most feature selection criteria, such as Information Gain (IG) and Maximum Discrimination (MD), can be easily incorporated into our approach. We evaluate our method's classification performance on several real-world benchmarks, compared with the state-of-the-art feature selection approaches. The superior results demonstrate the effectiveness of the proposed approach and further indicate its wide potential applications in data mining.

A novel sensor feature extraction based on kernel entropy component analysis for discrimination of indoor air contaminants

Component analysis techniques for feature extraction in multi-sensor system (electronic nose) have been studied in this paper. A novel nonlinear kernel based Renyi entropy component analysis method is presented to address the feature extraction problem in sensor array and improve the odor recognition performance of E-nose. Specifically, a kernel entropy component analysis (KECA) as a nonlinear dimension reduction technique based on the Renyi entropy criterion is presented in this paper. In terms of the popular support vector machine (SVM) learning technique, a joint KECA–SVM framework is proposed as a system for nonlinear feature extraction and multi-class gases recognition in E-nose community. In particular, the comparisons with PCA, KPCA and ICA based component analysis methods that select the principal components with respect to the largest eigen-values or correlation have been fully explored. Experimental results on formaldehyde, benzene, toluene, carbon monoxide, ammonia and nitrogen dioxide demonstrate that the KECA–SVM method outperforms other methods in classification performance of E-nose. The MATLAB implementation of this work is available online at http://www.escience.cn/people/lei/index.html

Research on Image Feature Extraction Method Based on Orthogonal Projection Transformation of Multi-Task Learning Technology

When the number of labeled training samples is very small, the sample information we can use would be very little. Because of this, the recognition rates of some traditional image recognition methods are not satisfactory. In order to use some related information that always exist in other databases, which is helpful to feature extraction and can improve the recognition rates, we apply multi-task learning to feature extraction of images. Our researches are based on transferring the projection transformation. Our experiments results on the public AR, FERET and CAS-PEAL databases demonstrate that the proposed approaches are more effective than the general related feature extraction methods in classification performance.