Online Classification Research Articles

Worst-case regret analysis of computationally budgeted online kernel selection

We study the problem of online kernel selection under computational constraints, where the memory or time of kernel selection and online prediction procedures is restricted to a fixed budget. In this paper, we analyze the worst-case lower bounds on the regret of online kernel selection algorithm with a subset of the observed examples, and design algorithms enjoying corresponding upper bounds. We also identify the condition under which online kernel selection with time constraints is different from that with memory constraints. To design algorithms, we reduce the problems to two sequential decision problems, that is, the problem of prediction with expert advice and the multi-armed bandit problem with an additional observation. Our algorithms invent some new techniques, such as memory sharing, hypothesis space discretization and decoupled exploration-exploitation scheme. Numerical experiments on online regression and classification are conducted to verify our theoretical results.

On Physical-Layer Authentication via Online Transfer Learning

This article introduces a novel physical-layer (PHY-layer) authentication scheme, called transfer learning-based PHY-layer authentication (TL-PHA), aiming to achieve fast online user authentication that is highly desired for latency-sensitive applications such as edge computing. The proposed TL-PHA scheme is characterized by incorporating with a novel convolutional neural network architecture, namely, the triple-pool network (TP-Net), for achieving lightweight and online classification, as well as effective data augmentation methods for generation of data set samples for the network model training. To assess the performance of the proposed scheme, we conducted two sets of experiments, including the one using computer-simulated channel data and the other utilizing real experiment data generated by our wireless testbed. The results demonstrate the superiority of the proposed scheme in terms of authentication accuracy, detection rate, and training complexity compared to all the considered counterparts.

A deep neural network model for multi-view human activity recognition.

Multiple cameras are used to resolve occlusion problem that often occur in single-view human activity recognition. Based on the success of learning representation with deep neural networks (DNNs), recent works have proposed DNNs models to estimate human activity from multi-view inputs. However, currently available datasets are inadequate in training DNNs model to obtain high accuracy rate. Against such an issue, this study presents a DNNs model, trained by employing transfer learning and shared-weight techniques, to classify human activity from multiple cameras. The model comprised pre-trained convolutional neural networks (CNNs), attention layers, long short-term memory networks with residual learning (LSTMRes), and Softmax layers. The experimental results suggested that the proposed model could achieve a promising performance on challenging MVHAR datasets: IXMAS (97.27%) and i3DPost (96.87%). A competitive recognition rate was also observed in online classification.

Catch Me If You Can: Online Classification for Near Real-Time Anomaly Detection in Business Process Event Streams

Near-real-time monitoring and classification of business process event streams is becoming more and more prominent. This also includes ensuring data quality for the application of downstream online process mining activities and therefore identify and classify incorrect process behavior of incoming event streams in an online setting, what is considered too little in existing approaches. In this paper, we present an online classification approach that supports monitoring and anomaly detection in event streams at the event level. Possible process drifts can be handled by an online learning workflow. By integrating two explanatory components, the results of the online classification are made transparent and comprehensible. Through a technical experiment, the performance of the classification approach is evaluated based on different data sets. Thereby, the classification model achieves an average F1 score of 0.877 with an average processing time of ∼15 ms per event.

A Novel Automatic Classification Method Based on the Hybrid Lightweight Shunt Network for Sintered Surfaces

Accurate control of sintering temperature is crucial for synthesizing high-quality iron ores. In practice, the temperature is usually regulated by experienced operators through adjusting igniters according to the class of the sintered surface. However, the manual method is difficult to meet the demand for real-time classification in a continuously running sintering process. In this article, to solve the above problem, we design a convolutional neural network with the hybrid lightweight shunt (HLWS-Net) for the online classification of sintered surfaces. Specifically, we introduce a lightweight shunt (LWS) module that guides the network to focus on feature cues at different scales with low computational complexity, avoiding the neglect of vital features in small regions. In addition, to reduce the information loss due to the lightweight structure, the Res-LWS module is proposed by combining the LWS module with the residual connection. Finally, the hybrid use of LWS and Res-LWS improves the classification performance of the network with little additional computational burden. To the best of our knowledge, this is the first method for sintering quality assessment from sintered surface images, which provides a new perspective to automate the sintering process. The experiments demonstrate that the proposed algorithm outperforms state-of-the-art deep learning classification methods with 92.1% classification accuracy, and the entire classification process takes only 0.038 seconds for a single image, which achieves a reasonable accuracy/speed trade-off.

Classification of Laser Footprint Based on Random Forest in Mountainous Area Using GLAS Full-Waveform Features

Full-waveform spaceborne laser altimeter can provide more characteristic parameters of the laser footprint and rich vertical structure information on the target surface. This technology has the potential for land-cover classification, especially in hard-to-reach mountain areas. Classifying the land types based on the returned waveform can provide a convenient way for the online classification needs and assess the quality of footprint used as the ground control point in photogrammetry. This article presents a random forest (RF) classification using geoscience laser altimeter system waveform, in the west-central Yunnan Province, China. First, an improved threshold wavelet is performed to denoise the waveform, and then Gaussian decomposition is used to extract the typical characteristic features of footprint. Second, an RF algorithm is implemented to clarify the footprints into five types: flat, building, terrace, forest, and mountain. Finally, quantitative analysis is conducted with producer's accuracy (PA), user's accuracy (UA), overall accuracy (OA), precision, recall rate, F1-score, and kappa coefficient to compare the performance of RF with other classifiers, including linear support vector machine (linear-SVM), radial basis function SVM (RBF-SVM), logistic regression (LR), K-nearest neighbor (KNN), and naive Bayes (NB). The results show that all the six methods can accurately classify the flat land with 100.00% PA and UA. The RF also has the best performances in other four land types, with PA and UA of 98.14% and 100.00%, 97.24% and 95.49%, 98.64% and 96.03%, and 94.64% and 100.00%, respectively. The OA, precision, recall, F1-score, and kappa coefficient for the RF are 97.95%, 97.73%, 98.30%, 97.99%, and 0.9737, respectively; while 83.45%, 82.55%, 82.98%, 81.16%, and 0.7765 for NB, which has the worst performance. LR performs better than RBF-SVM, linear-SVM, and KNN. It also observes worse classification accuracy for all methods when the waveforms are more complex.

Open Set Online Classification of Industrial Alarm Floods with Alarm Ranking

Open Set Online Classification of Industrial Alarm Floods with Alarm Ranking

BLENDED LEARNING AND FLIPPED CLASSROOM, TRENDS OF LEARNING METHODS DURING THE COVID-19 PANDEMIC

Blended learning is a learning method that uses slices of online learning systems (online) and face-to-face learning through video conferences. Flipped Classroom is a reverse classroom learning method, in which the teacher presents recorded material in the form of audio, video, presentation slides online on YouTube so that students can access and study before activities in class in the form of question and answer sessions, discussions. exercise, or other learning activities. This study aims to: (1) describe the classification of teachers application of the blended learning method in Islamic Educational Institutions, (2) describe the teacher's implementation/application of the flipped classroom learning method in Islamic Educational Institutions. The method used in this research is descriptive research with a quantitative approach. The object of research is the application of blended learning and flipped classroom in Islamic Education Institutions. The subjects to be studied are teachers at Madrasah Aliyah Negeri 3 Banjarmasin in the application of blended learning and flipped classrooms. Data collection using a questionnaire sheet. Data were analyzed using percentage analysis and classification of the application of blended learning and flipped classroom. The results of the study are (a) the frequency of application of blended learning in the classification: traditional 0 (0%), web facilitated 9 people (32%), blended 9 people (32%), and online classification 10 people (36%); (b) the frequency of application of the flipped classroom is 15 people (57%) and the traditional classroom is 12 people (43%).

An application for classifying perceptions on my health bank in Taiwan using convolutional neural networks and web-based computerized adaptive testing: A development and usability study.

Background:The classification of a respondent's opinions online into positive and negative classes using a minimal number of questions is gradually changing and helps turn techniques into practices. A survey incorporating convolutional neural networks (CNNs) into web-based computerized adaptive testing (CAT) was used to collect perceptions on My Health Bank (MHB) from users in Taiwan. This study designed an online module to accurately and efficiently turn a respondent's perceptions into positive and negative classes using CNNs and web-based CAT.Methods:In all, 640 patients, family members, and caregivers with ages ranging from 20 to 70 years who were registered MHB users were invited to complete a 3-domain, 26-item, 5-category questionnaire asking about their perceptions on MHB (PMHB26) in 2019. The CNN algorithm and k-means clustering were used for dividing respondents into 2 classes of unsatisfied and satisfied classes and building a PMHB26 predictive model to estimate parameters. Exploratory factor analysis, the Rasch model, and descriptive statistics were used to examine the demographic characteristics and PMHB26 factors that were suitable for use in CNNs and Rasch multidimensional CAT (MCAT). An application was then designed to classify MHB perceptions.Results:We found that 3 construct factors were extracted from PMHB26. The reliability of PMHB26 for each subscale beyond 0.94 was evident based on internal consistency and stability in the data. We further found the following: the accuracy of PMHB26 with CNN yields a higher accuracy rate (0.98) with an area under the curve of 0.98 (95% confidence interval, 0.97–0.99) based on the 391 returned questionnaires; and for the efficiency, approximately one-third of the items were not necessary to answer in reducing the respondents’ burdens using Rasch MCAT.Conclusions:The PMHB26 CNN model, combined with the Rasch online MCAT, is recommended for improving the accuracy and efficiency of classifying patients’ perceptions of MHB utility. An application developed for helping respondents self-assess the MHB cocreation of value can be applied to other surveys in the future.

Stress emotion classification using optimized convolutional neural network for online transfer learning dataset

Nowadays, deep learning methods with transfer learning (TL) makes ease of stress emotion classification tasks. Amongst, an optimized convolutional neural network with TL (OCNNTL) executes OCNN-based classification on emotion and stress data domains to learn high-level features at the top layers. However, it fails to handle the abrupt concept drift in real-time; besides, it end up with huge time complication while on gathering the required data and its transformation. To tackle the aforementioned concerns, a novel online OCNNTL (O2CNNTL) model is proposed; whereas, OCNNTL process initiates in the stress-emotion domain via the prior knowledge acquired by learning the training data both from the stress as well as the emotion domains. Moreover in O2CNNTL model, the concept-drifting data streams are taken into account for solving the online classification by the OCNN classifier; whereas, to enhance the learning efficiency a regularization learning technique is instigated on varied feature spaces. Thus, the proposed O2CNNTL achieves higher efficiency than the state-of-the-art models.

Towards Bio-Inspired Anomaly Detection Using the Cursory Dendritic Cell Algorithm

The autonomous and adaptable identification of anomalies in industrial contexts, particularly in the physical processes of Cyber-Physical Production Systems (CPPS), requires using critical technologies to identify failures correctly. Most of the existing solutions in the anomaly detection research area do not consider such systems’ dynamics. Due to the complexity and multidimensionality of CPPS, a scalable, adaptable, and rapid anomaly detection system is needed, considering the new design specifications of Industry 4.0 solutions. Immune-based models, such as the Dendritic Cell Algorithm (DCA), may provide a rich source of inspiration for detecting anomalies, since the anomaly detection problem in CPPS greatly resembles the functionality of the biological dendritic cells in defending the human body from hazardous pathogens. This paper tackles DCA limitations that may compromise its usage in anomaly detection applications, such as the manual characterization of safe and danger signals, data analysis not suitable for online classification, and the lack of an object-oriented implementation of the algorithm. The proposed approach, the Cursory Dendritic Cell Algorithm (CDCA), is a novel variation of the DCA, developed to be flexible and monitor physical industrial processes continually while detecting anomalies in an online fashion. This work’s contribution is threefold. First, it provides a comprehensive review of Artificial Immune Systems (AIS), focusing on AIS applied to the anomaly detection problem. Then, a new object-oriented architecture for the DCA implementation is described, enabling the modularity and abstraction of the algorithm stages into different classes (modules). Finally, the CDCA for the anomaly detection problem is proposed. The CDCA was successfully validated in two industrial-oriented dataset benchmarks for physical anomaly and network intrusion detection, the Skoltech Anomaly Benchmark (SKAB) and M2M using OPC UA. When compared to other algorithms, the proposed approach exhibits promising classification results. It was placed fourth on the SKAB scoreboard and presented a competitive performance with the incremental Dendritic Cell Algorithm (iDCA).

A Hybrid Online Classifier System for Internet Traffic Based on Statistical Machine Learning Approach and Flow Port Number

Internet traffic classification is a beneficial technique in the direction of intrusion detection and network monitoring. After several years of searching, there are still many open problems in Internet traffic classification. The hybrid classifier combines more than one classification method to identify Internet traffic. Using only one method to classify Internet traffic poses many risks. In addition, an online classifier is very important in order to manage threats on traffic such as denial of service, flooding attack and other similar threats. Therefore, this paper provides some information to differentiate between real and live internet traffic. In addition, this paper proposes a hybrid online classifier (HOC) system. HOC is based on two common classification methods, port-base and ML-base. HOC is able to perform an online classification since it can identify live Internet traffic at the same time as it is generated. HOC was used to classify three common Internet application classes, namely web, WhatsApp and Twitter. HOC produces more than 90% accuracy, which is higher than any individual classifiers.

Access-oblivious and Privacy-Preserving K Nearest Neighbors Classification in dual clouds

Massive data and data services (such as online classification) can be outsourced to the cloud for better processing and less operation cost. However, if the sensitive information is contained in outsourced data, and user’s requests and returned results are visible and controlled by the semi-trusted cloud server, this will lead to serious privacy leakage. To achieve secure and efficient outsourced classification services, both high security and low computation burden should be considered at the same time, but the existing works always focus on only one aspect of them. In this paper, a Privacy-Preserving K-Nearest-Neighbors Classification (PPKNNC) scheme based on two non-colluding servers is proposed. Specifically, two efficient privacy-preserving protocols and random permutation technique are used in the processing of user’s requests, which greatly reduces the computational cost and protects the access pattern. Besides, an Elgamal cryptosystem technique called proxy re-encryption is adopted to ensure that every user has an individual secret key, so that the exact key for data decryption is known only to the data owner and not to other entities. Finally, the security analysis and the experiment results further illustrate that the proposed scheme is superior to the existing works in terms of security, computational and communication costs.

Online Monitoring of Power Converter Degradation Using Deep Neural Network

Power semiconductor devices in the power converters used for motor drives are susceptible to wear-out and failure, especially when operated in harsh environments. Therefore, detection of degradation of power devices is crucial for ensuring the reliable performance of power converters. In this paper, a deep learning approach for online classification of the health states of the snubber resistors in the Insulated Gate Bipolar Transistors (IGBTs) in a three-phase Brushless DC (BLDC) motor drive is proposed. The method can locate one out of the six IGBTs experiencing a snubber resistor degradation problem by measuring the voltage waveforms of the three shunt resistors using voltage sensors. The range of the degradation of the snubber resistors for successful classification is also investigated. The off-the-shelf deep Convolutional Neural Network (CNN) architecture ResNet50 is used for transfer learning to determine which snubber resistor has degraded. The dataset for evaluating the above classification scheme of IGBT degradation is obtained by measuring the shunt voltage waveforms with varying snubber resistance and reference current. Then, the three-phase voltage waveforms are converted into greyscale images and RGB spectrogram images, which are later fed into the deep CNN. Experiments are carried out on the greyscale image dataset and the spectrogram image dataset using four-fold cross-validation. The results show that the proposed scheme can classify seven classes (one class for normal condition and six classes for abnormal condition in one of the six IGBTs in a three-phase BLDC drive) with over 95% average accuracy within a specific range of snubber resistance. Using grayscale images and using spectrogram-based RGB images yields similar accuracy.

Research on Classification Model of Panax notoginseng Taproots Based on Machine Vision Feature Fusion.

The existing classification methods for Panax notoginseng taproots suffer from low accuracy, low efficiency, and poor stability. In this study, a classification model based on image feature fusion is established for Panax notoginseng taproots. The images of Panax notoginseng taproots collected in the experiment are preprocessed by Gaussian filtering, binarization, and morphological methods. Then, a total of 40 features are extracted, including size and shape features, HSV and RGB color features, and texture features. Through BP neural network, extreme learning machine (ELM), and support vector machine (SVM) models, the importance of color, texture, and fusion features for the classification of the main roots of Panax notoginseng is verified. Among the three models, the SVM model performs the best, achieving an accuracy of 92.037% on the prediction set. Next, iterative retaining information variables (IRIVs), variable iterative space shrinkage approach (VISSA), and stepwise regression analysis (SRA) are used to reduce the dimension of all the features. Finally, a traditional machine learning SVM model based on feature selection and a deep learning model based on semantic segmentation are established. With the model size of only 125 kb and the training time of 3.4 s, the IRIV-SVM model achieves an accuracy of 95.370% on the test set, so IRIV-SVM is selected as the main root classification model for Panax notoginseng. After being optimized by the gray wolf optimizer, the IRIV-GWO-SVM model achieves the highest classification accuracy of 98.704% on the test set. The study results of this paper provide a basis for developing online classification methods of Panax notoginseng with different grades in actual production.

A Fast Online Classification Method of Solid Wood Floors Based on Stochastic Sampling and Machine Learning

Solid wood floors are widely used as an interior decoration material, and the color of solid wood surfaces plays a decisive role in the final decoration effect. Therefore, the color classification of solid wood floors is the final and most important step before laying. However, research on floor classification usually focuses on recognizing complex and diverse features but ignores execution speed, which causes common methods to not meet the requirements of online classification in practical production. In this paper, a new online classification method of solid wood floors was proposed by combining probability theory and machine learning. Firstly, a probability-based feature extraction method (stochastic sampling feature extractor) was developed to obtain rapid key features regardless of the disturbance of wood grain. The stochastic features were determined by a genetic algorithm. Then, an extreme learning machine—as a fast classification neural network—was selected and trained with the selected stochastic features to classify solid wood floors. Several experiments were carried out to evaluate the performance of the proposed method, and the results showed that the proposed method achieved a classification accuracy of 97.78% and less than 1 ms for each solid wood floor. The proposed method has advantages including a high execution speed, great accuracy, and flexible adaptability. Overall, it is suitable for online industry production.

Learning Geometry Information of Target for Visual Object Tracking with Siamese Networks.

Recently, Siamese architecture has been widely used in the field of visual tracking, and has achieved great success. Most Siamese network based trackers aggregate the target information of two branches by cross-correlation. However, since the location of the sampling points in the search feature area is pre-fixed in cross-correlation operation, these trackers suffer from either background noise influence or missing foreground information. Moreover, the cross-correlation between the template and the search area neglects the geometry information of the target. In this paper, we propose a Siamese deformable cross-correlation network to model the geometric structure of target and improve the performance of visual tracking. We propose to learn an offset field end-to-end in cross-correlation. With the guidance of the offset field, the sampling in the search image area can adapt to the deformation of the target, and realize the modeling of the geometric structure of the target. We further propose an online classification sub-network to model the variation of target appearance and enhance the robustness of the tracker. Extensive experiments are conducted on four challenging benchmarks, including OTB2015, VOT2018, VOT2019 and UAV123. The results demonstrate that our tracker achieves state-of-the-art performance.

Do AGN triggering mechanisms vary with radio power? – II. The importance of mergers as a function of radio power and optical luminosity

ABSTRACT Investigation of the triggering mechanisms of radio active galactic nuclei (radio AGN) is important for improving our general understanding of galaxy evolution. In the first paper in this series, detailed morphological analysis of high-excitation radio galaxies (HERGs) with intermediate radio powers suggested that the importance of triggering via galaxy mergers and interactions increases strongly with AGN radio power and weakly with optical emission-line luminosity. Here, we use an online classification interface to expand our morphological analysis to a much larger sample of 155 active galaxies (3CR radio galaxies, radio-intermediate HERGs, and Type 2 quasars) that covers a broad range in both 1.4 GHz radio power and [O iii] λ5007 emission-line luminosity. All active galaxy samples are found to exhibit excesses in their rates of morphological disturbance relative to 378 stellar-mass- and redshift-matched non-active control galaxies classified randomly and blindly alongside them. These excesses are highest for the 3CR HERGs (4.7σ) and Type 2 quasar hosts (3.9σ), supporting the idea that galaxy mergers provide the dominant triggering mechanism for these subgroups. When the full active galaxy sample is considered, there is clear evidence to suggest that the enhancement in the rate of disturbance relative to the controls increases strongly with [O iii] λ5007 emission-line luminosity but not with 1.4 GHz radio power. Evidence that the dominant AGN host types change from early-type galaxies at high radio powers to late-type galaxies at low radio powers is also found, suggesting that triggering by secular, disc-based processes holds more importance for lower-power radio AGN.

A Multi-Feature Ensemble Learning Classification Method for Ship Classification with Space-Based AIS Data

AIS (Automatic Identification System) is an effective navigation aid system aimed to realize ship monitoring and collision avoidance. Space-based AIS data, which are received by satellites, have become a popular and promising approach for providing ship information around the world. To recognize the types of ships from the massive space-based AIS data, we propose a multi-feature ensemble learning classification model (MFELCM). The method consists of three steps. Firstly, the static and dynamic information of the original data is preprocessed and features are then extracted in order to obtain static feature samples, dynamic feature distribution samples, time-series samples, and time-series feature samples. Secondly, four base classifiers, namely Random Forest, 1D-CNN (one-dimensional convolutional neural network), Bi-GRU (bidirectional gated recurrent unit), and XGBoost (extreme gradient boosting), are trained by the above four types of samples, respectively. Finally, the base classifiers are integrated by another Random Forest, and the final ship classification is outputted. In this paper, we use the global space-based AIS data of passenger ships, cargo ships, fishing boats, and tankers. The model gets a total accuracy of 0.9010 and an F1 score of 0.9019. The experiments prove that MFELCM is better than the base classifiers. In addition, MFELCM can achieve near real-time online classification, which has important applications in ship behavior anomaly detection and maritime supervision.

Adaptive online terrain classification method for mobile robot based on vibration signals

To improve the accuracy of terrain classification during mobile robot operation, an adaptive online terrain classification method based on vibration signals is proposed. First, the time domain and the combined features of the time, frequency, and time–frequency domains in the original vibration signal are extracted. These are adopted as the input of the random forest algorithm to generate classification models with different dimensions. Then, by judging the relationship between the current speed of the mobile robot and its critical speed, the classification model of different dimensions is adaptively selected for online classification. Offline and online experiments are conducted for four different terrains. The experimental results show that the proposed method can effectively avoid the self-vibration interference caused by an increase in the robot’s moving speed and achieve higher terrain classification accuracy.