Multi-label Support Vector Machine Research Articles

A concurrent fault diagnosis method for electric isolation valves in nuclear power plants based on rule-based reasoning and data-driven methods

In order to improve the fault diagnosis ability of electric isolation valves in nuclear power plants, especially for the diagnosis of concurrent faults, a novel concurrent fault diagnosis method based on rule reasoning and data-driven methods is proposed in this study. The concurrent fault diagnosis method is divided into two layers: the rule-based reasoning layer and data-driven layer. In order to realize the diagnosis of most concurrent faults, the rule-based reasoning layer is established by a rule inference model according to the failure mode and effects analysis (FMEA) of the electric isolation valve. In order to diagnose concurrent faults with mutual interference of fault features that cannot be diagnosed by rule-based reasoning layer, the data-driven layer is established by variational mode decomposition (VMD) and fast independent component analysis (Fast-ICA) method to separate concurrent fault signals and gate recurrent unit (GRU) method to identify fault patterns. After testing on the electric valve test bench and simulation data, multiple concurrent faults of the electric isolation valve are diagnosed accurately by the proposed concurrent fault diagnosis framework, including complex concurrent faults such as internal leakage and external leakage, and the framework can also work in a single fault diagnosis scenario without changing models. Compared with the concurrent fault diagnosis based on multi-label support vector machine, the accuracy of concurrent fault diagnosis is improved by about 25.3%. The research can provide a new idea for the concurrent fault diagnosis of electric isolation valves and other equipment.

Automated Matching of Patients to Clinical Trials: A Patient-Centric Natural Language Processing Approach for Pediatric Leukemia.

Matching patients to clinical trials is cumbersome and costly. Attempts have been made to automate the matching process; however, most have used a trial-centric approach, which focuses on a single trial. In this study, we developed a patient-centric matching tool that matches patient-specific demographic and clinical information with free-text clinical trial inclusion and exclusion criteria extracted using natural language processing to return a list of relevant clinical trials ordered by the patient's likelihood of eligibility. Records from pediatric leukemia clinical trials were downloaded from ClinicalTrials.gov. Regular expressions were used to discretize and extract individual trial criteria. A multilabel support vector machine (SVM) was trained to classify sentence embeddings of criteria into relevant clinical categories. Labeled criteria were parsed using regular expressions to extract numbers, comparators, and relationships. In the validation phase, a patient-trial match score was generated for each trial and returned in the form of a ranked list for each patient. In total, 5,251 discretized criteria were extracted from 216 protocols. The most frequent criterion was previous chemotherapy/biologics (17%). The multilabel SVM demonstrated a pooled accuracy of 75%. The text processing pipeline was able to automatically extract 68% of eligibility criteria rules, as compared with 80% in a manual version of the tool. Automated matching was accomplished in approximately 4 seconds, as compared with several hours using manual derivation. To our knowledge, this project represents the first open-source attempt to generate a patient-centric clinical trial matching tool. The tool demonstrated acceptable performance when compared with a manual version, and it has potential to save time and money when matching patients to trials.

Leak detection in water distribution network using machine learning techniques

ABSTRACT Leakage in the water distribution system (WDS) and its control has been challenging for water resources fraternity for management of precious water demand. This study examines an inverse engineering technique to find the leaks in water supply pipelines. The main objective of the study has been to identify the patterns of deviations in the pressure/flow in the network, due to a single leak in the network, by solving classification and regression problems using artificial neural networks (ANNs) and support vector machines (SVMs). The leak detections were solved using two scenarios, wherein, (a) only pressure measurements and (b) only flow measurements, are undertaken in the system. The multi-layered perceptron (MLP) model and multi-label multi-class SVM classification and regression models were developed and trained using the pressure and flow signals, separately. It was found that the ANN model performed better than the SVM model in pressure- and flow-based leak detection in both classification and regression problems. The model performance could also be improved by optimizing the number of inputs to the model during the training phase. The present study would be useful for water supply management while applying the techniques for minimizing the losses in the water supply network due to leakages.

Image Geo-Site Estimation Using Convolutional Auto-Encoder and Multi-Label Support Vector Machine

The estimation of an image geo-site solely based on its contents is a promising task. Compelling image labelling relies heavily on contextual information, which is not as simple as recognizing a single object in an image. An Auto-Encode-based support vector machine approach is proposed in this work to estimate the image geo-site to address the issue of misclassifying the estimations. The proposed method for geo-site estimation is conducted using a dataset consisting of 125 classes of various images captured within 125 countries. The proposed work uses a convolutional Auto-Encode for training and dimensionality reduction. After that, the acquired preprocessed input dataset is further processed by a multi-label support vector machine. The performance assessment of the proposed approach has been accomplished using accuracy, sensitivity, specificity, and F1-score as evaluation parameters. Eventually, the proposed approach for image geo-site estimation presented in this article outperforms Auto-Encode-based K-Nearest Neighbor and Auto-Encode-Random Forest methods.

Improving Recommendation for Effective Personalization in Context-Aware Data Using Novel Neural Network

The digital technologies that run based on users’ content provide a platform for users to help air their opinions on various aspects of a particular subject or product. The recommendation agents play a crucial role in personalizing the needs of individual users. Therefore, it is essential to improve the user experience. The recommender system focuses on recommending a set of items to a user to help the decision-making process and is prevalent across e-commerce and media websites. In Context-Aware Recommender Systems (CARS), several influential and contextual variables are identified to provide an effective recommendation. A substantial trade-off is applied in context to achieve the proper accuracy and coverage required for a collaborative recommendation. The CARS will generate more recommendations utilizing adapting them to a certain contextual situation of users. However, the key issue is how contextual information is used to create good and intelligent recommender systems. This paper proposes an Artificial Neural Network (ANN) to achieve contextual recommendations based on user-generated reviews. The ability of ANNs to learn events and make decisions based on similar events makes it effective for personalized recommendations in CARS. Thus, the most appropriate contexts in which a user should choose an item or service are achieved. This work converts every label set into a Multi-Label Classification (MLC) problem to enhance recommendations. Experimental results show that the proposed ANN performs better in the Binary Relevance (BR) Instance-Based Classifier, the BR Decision Tree, and the Multi-label SVM for Trip Advisor and LDOS-CoMoDa Dataset. Furthermore, the accuracy of the proposed ANN achieves better results by 1.1% to 6.1% compared to other existing methods.

Massive-Scale Aerial Photo Categorization by Cross-Resolution Visual Perception Enhancement.

Categorizing aerial photographs with varied weather/lighting conditions and sophisticated geomorphic factors is a key module in autonomous navigation, environmental evaluation, and so on. Previous image recognizers cannot fulfill this task due to three challenges: 1) localizing visually/semantically salient regions within each aerial photograph in a weakly annotated context due to the unaffordable human resources required for pixel-level annotation; 2) aerial photographs are generally with multiple informative attributes (e.g., clarity and reflectivity), and we have to encode them for better aerial photograph modeling; and 3) designing a cross-domain knowledge transferal module to enhance aerial photograph perception since multiresolution aerial photographs are taken asynchronistically and are mutually complementary. To handle the above problems, we propose to optimize aerial photograph's feature learning by leveraging the low-resolution spatial composition to enhance the deep learning of perceptual features with a high resolution. More specifically, we first extract many BING-based object patches (Cheng et al., 2014) from each aerial photograph. A weakly supervised ranking algorithm selects a few semantically salient ones by seamlessly incorporating multiple aerial photograph attributes. Toward an interpretable aerial photograph recognizer indicative to human visual perception, we construct a gaze shifting path (GSP) by linking the top-ranking object patches and, subsequently, derive the deep GSP feature. Finally, a cross-domain multilabel SVM is formulated to categorize each aerial photograph. It leverages the global feature from low-resolution counterparts to optimize the deep GSP feature from a high-resolution aerial photograph. Comparative results on our compiled million-scale aerial photograph set have demonstrated the competitiveness of our approach. Besides, the eye-tracking experiment has shown that our ranking-based GSPs are over 92% consistent with the real human gaze shifting sequences.

Simultaneous fault diagnosis for aircraft engine using multi-label learning

Fault detection and isolation system is crucial for the safety and reliability of aircraft engine. Traditional techniques of data-driven fault diagnosis for aircraft engine mainly focus on single fault diagnosis problems by means of the single-label learning strategy. However, the simultaneous fault diagnosis problems cannot be ignored in reality. In this research, two data-driven approaches based on multi-label learning and support vector machine are proposed to address the simultaneous fault diagnosis for an aircraft engine. Given that the simultaneous fault data are more difficult to obtain than single fault data, the proposed approaches have the ability to diagnose both single fault and simultaneous fault for aircraft engine when the fault diagnosis system is trained using single fault data only. The experimental results show that the proposed approaches can diagnose the simultaneous fault for an aircraft engine with high accuracy requiring low computation burden and a small number of single fault training data. In addition, the supplementary experiment confirms that the diagnosis accuracy of the proposed methods can be further improved by adding a small amount of the simultaneous fault data into the training dataset.

Complex Decay Prediction of Marine Machinery Using Multilabel SVM

Abstract In this article, a multilabel support vector machine (SVM)-based approach is investigated to address the simultaneous decay detection of the marine propulsion system. To verify the performance of the algorithm, we perform some experiments using a simulation dataset from a real-data validated numerical simulator of a Frigate. In particular, we try to train the model without simultaneous decay data, considering the great difficulty of obtaining simultaneous decay data in practice. The experimental results show that the proposed approach can identify the complex decay modes of the marine propulsion system effectively using only simple decay data in the training process. Introduction The propulsion system is considered to be the “heart” of a marine ship (Li et al. 2019a). Its safety and reliability are critical to the regular operation of the ship (Bayer et al. 2018; Cheliotis & Lazakis, 2018; Lazakis et al. 2016). However, performance decay may occur to the propulsion system due to the high humidity and high salt characteristics of the marine environment (Fang et al. 2018; Kang et al. 2019; Wang et al. 2019). The decay modes can be divided into single decay and simultaneous decay. Single decay indicates a simple decay mode that only one kind of decay occurs at a time, and simultaneous decay indicates a complex decay mode that multiple decays occur at the same time. To improve the safety and reliability of the marine propulsion system, researchers have proposed many related approaches from the perspective of fault diagnosis.

Automatic Diabetic Retinopathy Grading System Based on Detecting Multiple Retinal Lesions

Multi-label classification (MLC) is considered an essential research subject in the computer vision field, principally in medical image analysis. For this merit, we derive benefits from MLC to diagnose multiple grades of diabetic retinopathy (DR) from various colored fundus images, especially from multi-label (ML) datasets. Therefore, ophthalmologists can detect early signs of DR as well as various grades to initiate appropriate treatment and avoid DR complications. In this paper, we propose a comprehensive ML computer-aided diagnosis (CAD) system based on deep learning technique. The proposed system’s main contribution is to detect and analyze various pathological changes accompanying DR development in the retina without injecting the patient with dye or making expensive scans. The proposed ML-CAD system visualizes the different pathological changes and diagnoses the DR grades for the ophthalmologists. First, we eliminate noise, enhance quality, and standardize the sizes of the retinal images. Second, we differentiated between the healthy and DR cases by calculating the gray level run length matrix average in four different directions. The system automatically extracts the four changes: exudates, microaneurysms, hemorrhages, and blood vessels by utilizing a deep learning technique (U-Net). Next, we extract six features, which are the gray level co-occurrence matrix, areas of the four segmenting pathology variations, and the bifurcation points count of the blood vessels. Finally, the resulting features were afforded to an ML support vector machine (SVM) based on a classifier chain to differentiate the various DR grades. We utilized eight benchmark datasets (four of them are considered ML) and six different performance evaluation metrics to evaluate the proposed system’s performance. It achieved 95.1%, 91.9%, 86.1%, 86.8%, 84.7%, 86.2% for accuracy, area under the curve, sensitivity, specificity, positive predictive value, and dice similarity coefficient, respectively. The experiments show encouraging results as compared with other systems.

An Efficient Multi-Label SVM Classification Algorithm by Combining Approximate Extreme Points Method and Divide-and-Conquer Strategy

Excessive time complexity has severely restricted the application of support vector machine (SVM) in large-scale multi-label classification. Thus, this paper proposes an efficient multi-label SVM classification algorithm by combining approximate extreme points method and divide-and-conquer strategy (AEDC-MLSVM). The AEDC-MLSVM classification algorithm firstly uses the approximate extreme points method to obtain the representative set from the multi-label training data set. While persisting almost all the useful information of multi-label training set, representative set can effectively reduce the scale of multi-label training set. Secondly, to acquire an efficient multi-label SVM classification model, the SVM based on the improved divide-and-conquer strategy is trained on the representative set, which will further improve the training speed and classification performance. The improvement is reflected in two aspects. (1) The improved divide-and-conquer strategy is applied to divide the representative set into subsets and this can ensure that each representative subset contains a certain number of positive and negative instances. This will avoid singular problems and overcome computation load imbalance problem. (2) The different error cost (DEC) method is applied to overcome the label imbalance problem. Effective experiments have proved that the training and testing speed of AEDC-MLSVM classification algorithm can be accelerated substantially while ensuring the classification performance.

Multi-label optimal margin distribution machine

Multi-label support vector machine (Rank-SVM) is a classic and effective algorithm for multi-label classification. The pivotal idea is to maximize the minimum margin of label pairs, which is extended from SVM. However, recent studies disclosed that maximizing the minimum margin does not necessarily lead to better generalization performance, and instead, it is more crucial to optimize the margin distribution. Inspired by this idea, in this paper, we first introduce margin distribution to multi-label learning and propose multi-label Optimal margin Distribution Machine (mlODM), which optimizes the margin mean and variance of all label pairs efficiently. Extensive experiments in multiple multi-label evaluation metrics illustrate that mlODM outperforms SVM-style multi-label methods. Moreover, empirical study presents the best margin distribution and verifies the fast convergence of our method.

Content-oriented image quality assessment with multi-label SVM classifier

Image content is a fundamental attribute of images and plays an important role in human perception of image information. However, the influence of image content type, which is derived based on the classification of the image content, has been largely ignored in the image quality assessment (IQA). In this paper, a new IQA database based on the classification of image content is built. In particular, the database contains four content types, including landscape, human face, handcrafted scene and the hybrid scene. In total, 80 reference images with 20 images for each type of content are involved, and 1600 distorted images with mean opinion scores (MOSs) are generated by using five types and four levels of distortion. Furthermore, to classify these images, especially for the hybrid case, a Support Vector Machine (SVM) based multi-label (ML) classification is presented. Extensive experiments based on existing no reference IQA (NR-IQA) models show that content classification can greatly facilitate the image quality evaluation. The database and code are made publicly available at: https://github.com/jingchao17/Content-oriented-Database.

Predicting gastrointestinal drug effects using contextualized metabolic models.

Gastrointestinal side effects are among the most common classes of adverse reactions associated with orally absorbed drugs. These effects decrease patient compliance with the treatment and induce undesirable physiological effects. The prediction of drug action on the gut wall based on in vitro data solely can improve the safety of marketed drugs and first-in-human trials of new chemical entities. We used publicly available data of drug-induced gene expression changes to build drug-specific small intestine epithelial cell metabolic models. The combination of measured in vitro gene expression and in silico predicted metabolic rates in the gut wall was used as features for a multilabel support vector machine to predict the occurrence of side effects. We showed that combining local gut wall-specific metabolism with gene expression performs better than gene expression alone, which indicates the role of small intestine metabolism in the development of adverse reactions. Furthermore, we reclassified FDA-labeled drugs with respect to their genetic and metabolic profiles to show hidden similarities between seemingly different drugs. The linkage of xenobiotics to their transcriptomic and metabolic profiles could take pharmacology far beyond the usual indication-based classifications.

Performance-based fault detection approach for the dew point process through a fuzzy multi-label support vector machine

Nowadays, there are many fault detection approaches, which are taken into consideration in two categories including the data-driven and the model-based fault detection approaches. One of the well-known data-based fault detection techniques is the support vector machine that is proved to be a powerful approach in the classification. This one has weakness in dealing with a variety of complicated data. To address this concern, based on the investigation presented, an integration of the two approaches including the fuzzy and the multi-label SVM is proposed. In a word, the performance-based approach can classify the noisy and the multi-label data. This one is carried out for the dew point process with the cooling cycle in the real-world plant data in correspondence with the simulated plant via the HYSYS software environment. The proposed performance-based approach is about 10% percent more accurate than the conventional multi-label SVM in identifying the faults of the process.

Fast multi-label SVM training based on approximate extreme points

Fast multi-label SVM training based on approximate extreme points

AMI-Enabled Distribution Network Line Outage Identification via Multi-Label SVM

This letter proposes an effective data mining method for identifying distribution network line outages by leveraging data collected through Advanced Metering Infrastructure (AMI). The line outage identification method is developed based on a Multi-Label Support Vector Machine (ML-SVM) classification scheme that utilizes the status of customers’ smart meters as input data and accordingly identifies the outage/operational status of distribution lines. The $\text{F}_{\beta }$ -score is proposed to validate the performance of the classifier through numerical simulations.

Gene function prediction based on combining gene ontology hierarchy with multi-instance multi-label learning.

Gene function annotation is the main challenge in the post genome era, which is an important part of the genome annotation. The sequencing of the human genome project produces a whole genome data, providing abundant biological information for the study of gene function annotation. However, to obtain useful knowledge from a large amount of data, a potential strategy is to apply machine learning methods to mine these data and predict gene function. In this study, we improved multi-instance hierarchical clustering by using gene ontology hierarchy to annotate gene function, which combines gene ontology hierarchy with multi-instance multi-label learning frame structure. Then, we used multi-label support vector machine (MLSVM) and multi-label k-nearest neighbor (MLKNN) algorithm to predict the function of gene. Finally, we verified our method in four yeast expression datasets. The performance of the simulated experiments proved that our method is efficient.

DNS Tunneling Detection Method Based on Multilabel Support Vector Machine

DNS tunneling is a method used by malicious users who intend to bypass the firewall to send or receive commands and data. This has a significant impact on revealing or releasing classified information. Several researchers have examined the use of machine learning in terms of detecting DNS tunneling. However, these studies have treated the problem of DNS tunneling as a binary classification where the class label is either legitimate or tunnel. In fact, there are different types of DNS tunneling such as FTP-DNS tunneling, HTTP-DNS tunneling, HTTPS-DNS tunneling, and POP3-DNS tunneling. Therefore, there is a vital demand to not only detect the DNS tunneling but rather classify such tunnel. This study aims to propose a multilabel support vector machine in order to detect and classify the DNS tunneling. The proposed method has been evaluated using a benchmark dataset that contains numerous DNS queries and is compared with a multilabel Bayesian classifier based on the number of corrected classified DNS tunneling instances. Experimental results demonstrate the efficacy of the proposed SVM classification method by obtaining an f-measure of 0.80.

Fast Multi-Label Low-Rank Linearized SVM Classification Algorithm Based on Approximate Extreme Points

To solve the problem that traditional multi-label support vector machine (SVM) classification algorithm adopting nonlinear kernel has been severely restricted from being used on large-scale data sets, we propose fast multi-label low-rank-linearized SVM classification algorithm based on approximate extreme points (AEML-LLSVM). First, it adopts the approximate extreme points' method to obtain representative sets from the training data set. Then, the approximate extreme points' low-rank-linearized SVM (AELLSVM) is trained on the representative sets. The AELLSVM integrates the advantages of approximate extreme points' method and LLSVM. Experimental results on three large-scale multi-label data sets have proven that the training and the testing speed of AEML-LLSVM classification algorithm are greatly improved under the premise that its classification performance is similar to that of ML-LIBSVM classification algorithm and superior to that of other fast multi-label SVM classification algorithms.

Text recognition in scene image and video frame using Color Channel selection

In recent years, recognition of text from natural scene image and video frame has got increased attention among the researchers due to its various complexities and challenges. Because of low resolution, blurring effect, complex background, different fonts, color and variant alignment of text within images and video frames, etc., text recognition in such scenario is difficult. Most of the current approaches usually apply a binarization algorithm to convert them into binary images and next OCR is applied to get the recognition result. In this paper, we present a novel approach based on color channel selection for text recognition from scene images and video frames. In the approach, at first, a color channel is automatically selected and then selected color channel is considered for text recognition. Our text recognition framework is based on Hidden Markov Model (HMM) which uses Pyramidal Histogram of Oriented Gradient features extracted from selected color channel. From each sliding window of a color channel our color-channel selection approach analyzes the image properties from the sliding window and then a multi-label Support Vector Machine (SVM) classifier is applied to select the color channel that will provide the best recognition results in the sliding window. This color channel selection for each sliding window has been found to be more fruitful than considering a single color channel for the whole word image. Five different features have been analyzed for multi-label SVM based color channel selection where wavelet transform based feature outperforms others. Our framework has been tested on different publicly available scene/video text image datasets. For Devanagari script, we collected our own data dataset. The performances obtained from experimental results are encouraging and show the advantage of the proposed method.