Classification Accuracy Of Method Research Articles

Automatic recognition of different types of acute leukaemia in peripheral blood by image analysis

AimsMorphological differentiation among different blast cell lineages is a difficult task and there is a lack of automated analysers able to recognise these abnormal cells. This study aims to develop...

Kernel Spherical K-Means and Support Vector Machine for Acute Sinusitis Classification

Acute sinusitis is an inflammation of the sinus which causes the cavity around the sinus to swells due to accumulated mucus. It makes the patient experience difficulty in breathing through the nose. Generally, it is caused by the common cold, and in most cases, the patient recovers within seven to ten days. However, persistent acute sinusitis can cause severe infections and other complications. Therefore, it requires timely detection and more accurate method of classification. Many techniques have been used to classify acute sinusitis but, in this study, the machine learning methods which includes Kernel Spherical K-Means (KSPKM) and Support Vector Machine (SVM) was applied. SPKM is the application of K-Means, in this research, it was modified by changing the inner product with kernel function to ensure linear data separation on higher dimensions for the maximization of SPKM performance. The SVM is a binary classification method that helps to create a model with good generalization ability. We used CT scan result data from RSCM, Central Jakarta. Simulations were performed with different percentage of training data. The results were compared in terms of Accuracy and Running Time. The score showed that the performance of KSPKM attained an accuracy rate of 97%, while SVM reached 90%.

Toward an Alignment-Free Method for Feature Extraction and Accurate Classification of Viral Sequences.

The classification of pathogens in emerging and re-emerging viruses represents major interests in taxonomic studies, functional genomics, host-pathogen interplay, prevention, and disease treatments. It consists of assigning a given sequence to its related group of known sequences sharing similar characteristics and traits. The challenges to such classification could be associated with several virus properties including recombination, mutation rate, multiplicity of motifs, and diversity. In domains such as pathogen monitoring and surveillance, it is important to detect and quantify known and novel taxa without exploiting the full and accurate alignments or virus family profiles. In this study, we propose an alignment-free method, CASTOR-KRFE, to detect discriminating subsequences within known pathogen sequences to classify accurately unknown pathogen sequences. This method includes three major steps: (1) vectorization of known viral genomic sequences based on k-mers to constitute the potential features, (2) efficient way of pattern extraction and evaluation maximizing classification performance, and (3) prediction of the minimal set of features fitting a given criterion (threshold of performance metric and maximum number of features). We assessed this method through a jackknife data partitioning on a dozen of various virus data sets, covering the seven major virus groups and including influenza virus, Ebola virus, human immunodeficiency virus 1, hepatitis C virus, hepatitis B virus, and human papillomavirus. CASTOR-KRFE provides a weighted average F-measure >0.96 over a wide range of viruses. Our method also shows better performance on complex virus data sets than multiple subsequences extractor for classification (MISSEL), a subsequence extraction method, and the Discriminative mode of MEME patterns extraction tool.

Characterization of Impulse Oscillometric Measures of Respiratory Small Airway Function in Children

Children suffering from asthma are often undiagnosed or misdiagnosed as their symptoms are similar to other respiratory conditions. Spirometry, the golden pulmonary function test used for asthma diagnosis, is often unsuitable for young children since it requires them to perform extreme inhalation and exhalation maneuvers. Impulse Oscillometry (IOS) is an effortless, child-friendly, sensitive, and reliable testing technique that could be used in the effective diagnosis of asthma. However, the IOS requires a deep understanding of the mechanical and/or equivalent electrical circuit models of the human respiratory system, which hinders its broad acceptance and utility in clinics. This paper presents a data characterization study based on the statistical assessment of the IOS parameters. The main focus is to investigate four different manifestations of pulmonary conditions in children due to peripheral obstruction: Asthma (A), Small Airway Impairment (SAI), Possible Small Airway Impairment (PSAI), and Normal (N). The objective of this investigation is to pave the way for the feature selection stage of our future computer-aided classification work to distinguish between lung dysfunction and healthy lung function in children by identifying those IOS parameters that are most sensitive to discriminate between the different respiratory conditions mentioned above. Our ultimate goal is to facilitate the interpretation of the impulse oscillometric test results and provide clinicians with a reliable and proven method for accurate classification of children's lung function for an early asthma detection, diagnosis, and control.

Automatic cost-effective method for land cover classification (ALCC)

The need for the detection and monitoring of changes in the environment is greater today than ever before. Through classification we can obtain insights into the state of the land surface. No known classification methods are fully automated, and their implementation requires preprocessing and postprocessing. This research provides a novel, fully automatic and cost-effective land cover classification method (ALCC). This novel automatic method does not require prior knowledge of the terrain or the assignment of training samples. The ALCC method is based on unsupervised classification methods, which is performed over the spectral indices rasters and six Landsat-8 30 m spatial resolution bands. The method was tested in three different study areas. Furthermore, all three study areas were classified by common supervised classification methods, namely, the Maximum Likelihood Classification (MLC) and the Random Forests (RF) method. For comparison accuracy, assessment of the three applied classification methods, namely, the figure of merit, overall agreement, omission and commission, were used. The results show that the overall agreement of the new automatic classification method for the Rijeka, Zagreb and Sarajevo study areas is 90.0%, 89.5% and 89.9%, respectively, and the overall agreement always falls between the overall agreement of the MLC method (88.1%, 88.9% and 86.7%, respectively) and the overall agreement of the RF method of classification (91.7%, 90.4% and 90.2%, respectively). These results confirm that this new automatic, cost-effective and accurate land cover classification method can be easily applied for numerous remote sensing applications.

Real-Time Multiple Event Detection and Classification in Power System Using Signal Energy Transformations

Real-time multiple event analysis is important for reliable situational awareness and secure operation of the power system. Multiple sequential events can induce complex superimposed pattern in the data and are challenging to analyze in real time. This paper proposes a method for accurate detection, temporal localization, and classification of multiple events in real time using synchrophasor data. For detection and temporal localization, a Teager–Kaiser energy operator (TKEO) based method is proposed. For event classification, a time series classification based method using energy similarity measure (ESM) is proposed. The proposed method is tested for simulated multiple event cases in the IEEE-118 bus system using DigSilent/PowerFactory and real PMU data for the Indian grid.

A hierarchical method based on weighted extreme gradient boosting in ECG heartbeat classification

Background and objectiveElectrocardiogram (ECG) is a useful tool for detecting heart disease. Automated ECG diagnosis allows for heart monitoring on small devices, especially on wearable devices. In order to recognize arrhythmias automatically, accurate classification method for electrocardiogram (ECG) heartbeats was studied in this paper. MethodsBased on weighted extreme gradient boosting (XGBoost), a hierarchical classification method is proposed. A large number of features from 6 categories are extracted from the preprocessed heartbeats. Then recursive feature elimination is used for selecting features. Afterwards, a hierarchical classifier is constructed in classification stage. The hierarchical classifier is composed of threshold and XGBoost classifiers. And the XGBoost classifiers are improved with weights. ResultsThe method was applied to an inter-patient experiment conforming AAMI standard. The obtained sensitivities for normal (N), supraventricular (S), ventricular (V), fusion (F), and Unknown beats (Q) were 92.1%, 91.7%, 95.1%, and 61.6%. Positive predictive values of 99.5%, 46.2%, 88.1%, and 15.2% were also provided for the four classes. ConclusionsXGBoost was improved and firstly introduced in single heartbeat classification. A comparison showed the effectiveness of the novel method. The method was more suitable for clinical application as both high positive predictive value for N class and high sensitivities for abnormal classes were provided.

Determination of Opium Poppy (Papaver Somniferum) Parcels Using High-Resolution Satellite Imagery

Narcotic plants contain substances that cause unusual excitation and subsequent depression of the central nervous system. Many narcotic plants contain substances that have medicinal properties and are used primarily as pain relievers. Opium poppy (Papaver somniferum) is one of the most cultivated narcotic plants. The planning of Opium poppy growing is controlled by United Nations and Drugs and Medicines Control Program. This study was conducted to establish a basis for determining the traceability of poppy cultivating areas using remote sensing in the large plain. A very high-resolution QuickBird-2 satellite image was used in the study. The software of ERDAS Imagine and eCognition Developer was performed for image processing and classification. A variety of classification methods were performed on the satellite image. ArcGIS software was used for accuracy assessment, ground control, and map production of the poppy cultivating area. The accuracy of classification methods was compared. The producer–user accuracy was estimated as 97.99% using spectral difference sub-segmentation process of multiresolution segmentation algorithm in object-based classifications method. This algorithm can be a practical approach for determining of opium poppy parcels in the large plain.

A density-watershed algorithm (DWA) method for robust, accurate and automatic classification of dual-fluorescence and four-cluster droplet digital PCR data.

Droplet digital PCR (ddPCR) is a single-molecule amplification technology with broad applications in precision medicine and clinical diagnosis. Dual-fluorescence and four-cluster ddPCR (two/four-ddPCR) assay is an effective way to quantify copy numbers. Currently, two/four-ddPCR data are usually classified with manual thresholds. For clinical applications, automatic and accurate methods are required to avoid subjectivity in diagnosis. Although there are some automatic classification algorithms, their accuracy and robustness still need to be improved to meet the needs of clinical diagnosis. Therefore, a new method is in high demand to automatically classify two/four-ddPCR data in an accurate and robust way. Here, a novel density-watershed algorithm (DWA) method was developed for the accurate, automatic and unsupervised classification of two/four-ddPCR data. First, data gridding was applied to a scatter plot of the fluorescence signal intensity to calculate data densities. Based on the data densities, the watershed algorithm was used to divide the gridded scatter plot into isolated regions automatically. Next, an optimal cluster pattern was determined based on these isolated regions, and excess regions were merged. Finally, the two/four-ddPCR data were classified based on the merged regions, and DNA template copy numbers were calculated accordingly. Using the DWA method for the quantification of both wild types and mutants of epidermal growth factor receptor (EGFR) L858R and T790M, the classification results were highly consistent with expectations, and significantly better than commonly-used automatic algorithms for now. The computed template copy numbers scaled proportionally to the relative concentration of input templates (r2 > 0.998) in four orders of magnitude with a good reproducibility, and achieved a limit of detection over 40 times lower than the commonly-used automatic algorithms. Furthermore, the DWA method was validated on 254 clinical DNA samples derived from frozen tissues, formalin-fixed paraffin-embedded tissues and peripheral blood. In most cases, the DWA method derived accurate and valid classification results. This highly effective DWA method may be widely used for automatically classifying two/four-ddPCR data, and it will greatly promote the application of ddPCR in clinical diagnosis.

Sparse Representation and Collaborative Representation? Both Help Image Classification

Image classification has attracted more and more attention. During the past decades, image classification has shown growing interest in representation-based classification methods, such as sparse representation-based classification and collaborative representation-based classification. However, the available representation-based methods still suffer from some problems. Especially, most methods only consider the shared representation of a test image. In this paper, we propose an elastic-net regularized regression algorithm (ENRR) for image classification. Specifically, our proposed method combines shared sparse representation with class specific collaborative representation when representing the test sample. Moreover, we extend the proposed ENRR to arbitrary kernel space to achieve better classification performance due to specificities and complexities of original images. The extensive experiments on face recognition datasets, handwritten recognition datasets, and remote sensing image datasets clearly demonstrate that the proposed ENRR outperforms several conventional methods in classification accuracy.

Spur Gear Fault Diagnosis Using a Multilayer Gated Recurrent Unit Approach With Vibration Signal

The fault diagnosis of the gearbox is a complex and important work. In this paper, a multilayer gated recurrent unit (MGRU) method is proposed for spur gear fault diagnosis, that is, three-layer gated recurrent unit (GRU). The vibration signals are firstly monitored on the test bench, and then extracted in both time domain and time-frequency domain. Finally, MGRU is used to learn representation and classification. The MGRU can improve the representation of information and identify the features of fault types more precisely with the increasing number of layers. The proposed method was tested by two spur gears with 10 state modes. To evaluate the method's classification accuracy, four methods were utilized for comparison, i.e., the GRU, long short-term memory (LSTM), multilayer LSTM (MLSTM), and support vector machine (SVM), respectively. In addition, the separability and robustness analysis are also discussed for the proposed MGRU performance. All of the results exhibited that the proposed MGRU approach is effective for spur gear fault diagnosis.

An Accurate Sleep Stages Classification Method Based on State Space Model

The classification of sleep stages is the process which helps to evaluate the quality of sleep and detect the sleep related disorders. Through analyzing the electroencephalography, the sleep stages can be discriminated manually by specialists. However, this can be a laboriousness work because of the huge datasets. Until now, several studies have been conducted based on the automatic analysis of electroencephalography. Still, as the development of wearable technology, there is a need for an accurate and single-channel electroencephalography based sleep stages identification system. In this paper, a state-space based sleep stages classification method is proposed using the proposed model based essence features extraction method. This method employed the state-space model to establish the intrinsic models based on the single-channel electroencephalography, from which the features used for further classification can be extracted. For 2-stage to 6-stage classification of sleep states, the verification system can achieve 98.6%, 94.9%, 93.0%, 92.3%, 91.8% accuracy on the Sleep-EDF database, and also reach 94.9%, 87.7%, 82.7%, 80.9%, 78.2% on Dreams Subjects database.

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.

Classification of Satellite Images in Assessing Urban Land Use Change Using Scale Optimization in Object-Oriented Processes (A Case Study: Ardabil City, Iran)

By using satellite imagery, the recognition and evaluation of various phenomena and extraction of information necessary for the planning of land resources or other purposes are easily accomplished. The purpose of this study is to compare the efficiency of seven commonly used methods of monitored classification of satellite data to evaluate land use changes using TM and OLI Landsat, IRS, Spot5 and Quick Bird bands as well as different color combinations of these images to detect agricultural land, residential areas and aquatic areas using object-oriented processing. Digital processing of satellite images was carried out in 1998 and 2016 using advanced methods. Training samples were extracted in five user classes by eCognition software using segmentation scale optimization, different color combinations and coefficients of shape and compression. The appropriate segmentation scale for arable land, human complications and the blue areas were, respectively, 50, 8 and 10. Then each image was classified separately using seven methods and extracted samples, and efficiency of each classification method was obtained by calculating two general health and Kappa coefficients. The results show that the accuracy of each classification method and the neural network with a total accuracy of 94.475 and Kappa coefficient of 92.095 were selected as the most accurate classification method. These results show that the sampling of educational samples with proper precision of the classes in the images and dependency probability of each satellite images pixel can be useful in classifying group available in helpful area.

Thin Cap Fibroatheroma Detection in Virtual Histology Images Using Geometric and Texture Features

Atherosclerotic plaque rupture is the most common mechanism responsible for a majority of sudden coronary deaths. The precursor lesion of plaque rupture is thought to be a thin cap fibroatheroma (TCFA), or “vulnerable plaque”. Virtual Histology-Intravascular Ultrasound (VH-IVUS) images are clinically available for visualising colour-coded coronary artery tissue. However, it has limitations in terms of providing clinically relevant information for identifying vulnerable plaque. The aim of this research is to improve the identification of TCFA using VH-IVUS images. To more accurately segment VH-IVUS images, a semi-supervised model is developed by means of hybrid K-means with Particle Swarm Optimisation (PSO) and a minimum Euclidean distance algorithm (KMPSO-mED). Another novelty of the proposed method is fusion of different geometric and informative texture features to capture the varying heterogeneity of plaque components and compute a discriminative index for TCFA plaque, while the existing research on TCFA detection has only focused on the geometric features. Three commonly used statistical texture features are extracted from VH-IVUS images: Local Binary Patterns (LBP), Grey Level Co-occurrence Matrix (GLCM), and Modified Run Length (MRL). Geometric and texture features are concatenated in order to generate complex descriptors. Finally, Back Propagation Neural Network (BPNN), kNN (K-Nearest Neighbour), and Support Vector Machine (SVM) classifiers are applied to select the best classifier for classifying plaque into TCFA and Non-TCFA. The present study proposes a fast and accurate computer-aided method for plaque type classification. The proposed method is applied to 588 VH-IVUS images obtained from 10 patients. The results prove the superiority of the proposed method, with accuracy rates of 98.61% for TCFA plaque.

Detection and classification of whales calls using band-limited energy detection and transfer learning

The Monterey Bay Aquarium Research Institute has been recording since July 2015 almost continuously at the Monterey Accelerated Research System (MARS) cabled observatory in Monterey Bay, California, USA. This long-term recording contains thousands of whale calls to help further our understanding of interannual, seasonal, and diel patterns. Here we report on our highly accurate detection and classification method developed to classify blue whale A, B, and D calls and Fin whale 20 Hz pulses. The foundation of the method is a computationally efficient and tuned decimation filter to convert the broadband hydrophone 128 kHz signal to 2 kHz which preserves the low-frequency signal and avoids any high-frequency aliasing. Detection is done using a band-limited-energy-detection filter to find potential calls in the decimated data. Spectrograms are then generated for potential calls and enhanced with local image normalization followed by smoothing by convolving in either time or frequency. Classification is done using the Google Inception v3 model with a transfer learning method. Overall, false positive rates are very low despite variability in whale call shape and background noise.

A novel projection nonparallel support vector machine for pattern classification

In this paper, we propose a novel nonparallel classifier termed as projection nonparallel support vector machine (PNPSVM), which is fully different from the existing nonparallel classifiers. The new classifier needs two steps to obtain the optimal proximal hyperplanes. The first step is to obtain two projection directions, which could achieve maximum class separability by minimizing the within-class distance and maximizing the between-class distance simultaneously, to be treated as the normal vectors of the optimal proximal hyperplanes, and the second step is to determine the specific locations of the optimal proximal hyperplanes based on an appropriate central sample. Furthermore, the improved successive overrelaxation (SOR) algorithm is applied to solve our PNPSVM. The incomparable advantages of this paper can be summarized as follows: (1) implementing the structural risk minimization principle in the primal problems; (2) utilizing the potential structural information of data by considering both the tightness between the similar patterns and the discrepancy between the dissimilar pairs; (3) the kernel trick can be applied directly since the dual problems have the similar elegant formulation as that of standard support vector machine; (4) SOR technique is introduced to solve our optimization problems. The comprehensive experimental results on an artificial dataset and twenty-four UCI datasets demonstrate the effectiveness of our method in classification accuracy.

Land-cover mapping using Random Forest classification and incorporating NDVI time-series and texture: a case study of central Shandong

ABSTRACTLand-cover mapping in complex farming area is a difficult task because of the complex pattern of vegetation and rugged mountains with fast-flowing rivers, and it requires a method for accurate classification of complex land cover. Random Forest classification (RFC) has the advantages of high classification accuracy and the ability to measure variable importance in land-cover mapping. This study evaluates the addition of both normalized difference vegetation index (NDVI) time-series and the Grey Level Co-occurrence Matrix (GLCM) textural variables using the RFC for land-cover mapping in a complex farming region. On this basis, the best classification model is selected to extract the land-cover classification information in Central Shandong. To explore which input variables yield the best accuracy for land-cover classification in complex farming areas, we evaluate the importance of Random Forest variables. The results show that adding not only multi-temporal imagery and topographic variables but also GLCM textural variables and NDVI time-series variables achieved the highest overall accuracy of 89% and kappa coefficient (κ) of 0.81. The assessment of the importance of a Random Forest classifier indicates that the key input variables include the summer NDVI followed by the summer near-infrared band and the elevation, along with the GLCM-mean, GLCM-contrast.

GeneCT: a generalizable cancerous status and tissue origin classifier for pan-cancer biopsies.

Tissue biopsy is commonly used in cancer diagnosis and molecular studies. However, advanced skills are required for determining cancerous status of biopsies and tissue origin of tumor for cancerous ones. Correct classification is essential for downstream experiment design and result interpretation, especially in molecular cancer studies. Methods for accurate classification of cancerous status and tissue origin for pan-cancer biopsies are thus urgently needed. We developed a deep learning-based classifier, named GeneCT, for predicting cancerous status and tissue origin of pan-cancer biopsies. GeneCT showed high performance on pan-cancer datasets from various sources and outperformed existing tools. We believe that GeneCT can potentially facilitate cancer diagnosis, tumor origin determination and molecular cancer studies. GeneCT is implemented in Perl/R and supported on GNU/Linux platforms. Source code, testing data and webserver are freely available at http://sunlab.cpy.cuhk.edu.hk/GeneCT/. Supplementary data are available at Bioinformatics online.

Incremental one‐class classification on stationary data stream using two‐quarter sphere

AbstractData stream is a sequence of data that has unique features. In many data streams, data from one concept is available, and detection of other types of data in data stream is an essential task. One‐class classification is a famous approach to data classification when data from one class is accessible. The principal task of one‐class classification is separating input data in two different parts: target and outlier data. One of the main challenges concerning data classification on the stationary data streams is the response time, which causes negative effects on effective runtime. Most classifiers, that have been proposed on Rd feature space, solve a quadratic problem for classification that leads to extreme runtime increase. In this paper, an incremental one‐class classification on stationary data streams is proposed using two‐quarter sphere (IOCTQ) in order to achieve lower computation cost of classification time, and a linear optimization problem is solved. IOCTQ divides one data classification problem into two data classifiers, and each data point will be individually classified in one‐quarter sphere. The two‐quarter spheres can be run parallel. The results of the experiments have been compared with state‐of‐the‐art methods and show superiority of the IOCTQ method in classification accuracy and time complexity.