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Online Active Learning Framework for Data Stream classification With Density-Peaks Recognition

IEEE ACCESS 2023年 11卷 27853-27864页

作者： Zhang, Kuangyan Liu, Sanmin Chen, Yanfei Anhui Polytech Univ Sch Comp & Informat Wuhu 241000 Peoples R China

This paper considers a practical scenario - data stream classification, and uses online active learning (OAL) to assist the classifier. Most active learning are proposed based on label uncertainty, but the methods based on representativeness are faced with difficulties in stream scenarios. The sampling criteria based on representativeness are usually fixed, which makes it difficult for them to work effectively in dynamic sample spaces, especially for concept drift streams. This paper devises a novel OAL framework based on sample representativeness. The framework uses local nearest-neighbor relation to measure the representativeness of unlabeled samples and divides the maximum influence space of the representative sample. We also develop an independent mechanism to identify and store short-term cluster fragments to ensure the integrity of information. The framework is deployed to the online environment and adapts to any incremental learner. Simulation experiments on multiple datasets and classifiers show that our method can help primary classifier deal with data stream environment effectly. Compared with other online active learning, the method can achieve more stable accuracy and better anti-noise ability under fewer budget.

关键词： Clustering algorithms Uncertainty classification algorithms Data mining Training data Engines Unsupervised learning Data stream classification online active learning density-peak clustering cluster fragmentation

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An Integrated Nature-Inspired Algorithm Hybridized Adaptive Broad Learning System for Disease classification

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IEEE ACCESS 2023年 11卷 31636-31656页

作者： Parhi, Pournamasi Bisoi, Ranjeeta Dash, Pradipta Kishore Siksha O Anusandhan Deemed be Univ Dept Comp Sci Engn Bhubaneswar 751030 Odisha India Siksha O Anusandhan Deemed be Univ Multidisciplinary Res Cell Bhubaneswar 751030 Odisha India

classification of different deadly diseases using machine learning algorithms helps in making the health care system more robust. This, not only reduces the human errors during diagnosis of disease due to inexperience but also helps the physician for taking an emergency action earlier to biopsy. As the genomic data undergoes through the malediction of excessive dimension problem, both the selection of the remarkable genes and classification of these data efficiently still remain as a demanding research problem. To obtain the notable features from high dimensional data, by using a nature-inspired algorithm, is a Non-deterministic Polynomial-time (NP)-Hard problem. Therefore, a researcher can apply new algorithm to solve this issue. In this suggested approach, an integrated natured-inspired algorithm i.e., Sine-Cosine (SC) based Monarch Butterfly Optimization (SC-MBO) is merged with an Adaptive Broad Learning System (ABLS) called SC-MBO-ABLS, to find out the most notable genetic features and classify the genomic data at the same time. In the preliminary stage, a feature extraction method i.e. Kernel based Fisher Score (K-FS) is applied to extract a notable key gene subset. Then, this extracted key gene subset goes through further execution with the SC-MBO-ABLS method. To examine the effectiveness of the presented method, ten genomic datasets are considered. Here, several performance evaluators (i.e., Precision, Matthews Correlation Coefficient, Sensitivity, Kappa, Specificity, and F-score) are used for a neutral estimation of the presented approach. This presented model is compared with SC-MBO wrapped Multilayer Perceptron (SC-MBO-MLP), SC-MBO wrapped Extreme Learning Machine (SC-MBO-ELM), and SC-MBO wrapped Kernel Extreme Learning Machine (SC-MBO-KELM). Further, sixteen existing standard models are compared to prove the supremacy of the presented method. Moreover, an analysis of Variance i.e., ANOVA test is carried out for statistical evaluation of the proposed work.

关键词： Feature extraction classification algorithms Bioinformatics Learning systems Genomics Metaheuristics Machine learning algorithms Genomic data high dimensionality notable genes feature extraction kernel fisher score classification sine-cosine adaptive broad learning system integrated monarch butterfly

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An Ensemble Learning Approach for Land Use/Land Cover classification of Arid Regions for Climate Simulation: A Case Study of Xinjiang, Northwest China

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2023年 16卷 2413-2426页

作者： Du, Haoyang Li, Manchun Xu, Yunyun Zhou, Chen Nanjing Univ Sch Geog & Ocean Sci Dept Geog Informat Sci Nanjing 210023 Peoples R China Nanjing Univ Sch Geog & Ocean Sci Jiangsu Prov Key Lab Geog Informat Technol Nanjing 210023 Peoples R China Nanjing Univ Dept Geog Informat Sci Nanjing 210023 Peoples R China Nanjing Univ Collaborat Innovat Ctr South Sea Studies Nanjing 210093 Peoples R China Nanjing Univ Collaborat Innovat Ctr Novel Software Technol & In Nanjing 210023 Peoples R China

Accurate classifications of land use/land cover (LULC) in arid regions are vital for analyzing changes in climate. We propose an ensemble learning approach for improving LULC classification accuracy in Xinjiang, northwest China. First, multisource geographical datasets were applied, and the study area was divided into Northern Xinjiang, Tianshan, and Southern Xinjiang. Second, five machine learning algorithms-k-nearest neighbor, support vector machine (SVM), random forest (RF), artificial neural network (ANN), and C4.5-were chosen to develop different ensemble learning strategies according to the climatic and topographic characteristics of each subregion. Third, stratified random sampling was used to obtain training samples and optimal parameters for each machine learning algorithm. Lastly, each derived approach was applied across Xinjiang, and subregion performance was evaluated. The results showed that the LULC classification accuracy achieved across Xinjiang via the proposed ensemble learning approach was improved by >= 6.85% compared with individual machine learning algorithms. By specific subregion, the accuracies for Northern Xinjiang, Tianshan, and Southern Xinjiang increased by >= 6.70%, 5.87%, and 6.86%, respectively. Moreover, the ensemble learning strategy combining four machine learning algorithms (i.e., SVM, RF, ANN, and C4.5) was superior across Xinjiang and Tianshan;whereas, the three-algorithm (i.e., SVM, RF, and ANN) strategy worked best for the Northern and Southern Xinjiang. The innovation of this study is to develop a novel ensemble learning approach to divide Xinjiang into different subregions, accurately classify land cover, and generate a new land cover product for simulating climate change in Xinjiang.

关键词： Ensemble learning classification algorithms Remote sensing Support vector machines Machine learning algorithms Climate change Radio frequency Arid areas China ensemble learning land use land cover classification machine learning

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Imbalanced Data classification Method Based on LSSASMOTE

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IEEE ACCESS 2023年 11卷 32252-32260页

作者： Wang, Zhi Liu, Qicheng Yantai Univ Sch Comp & Control Engn Yantai 264000 Shandong Peoples R China

Imbalanced data exist extensively in the real world, and the classification of imbalanced data is a hot topic in machine learning. In order to classify imbalanced data more effectively, an oversampling method named LSSASMOTE is proposed in this paper. First, the kernel function parameters and penalty parameters of the support vector machine (SVM) were optimized using levy sparrow search algorithm (LSSA), and a fitness function was correspondingly designed. Then, during the optimization process, SMOTE sampling rate was combined, and LSSA iteration was used to select the best combination of SVM parameters and SMOTE sampling rate. In addition, the oversampled samples were noise processed by Tomek Link. In this case, the LSSASMOTE+SVM classification model was constructed to classify the imbalanced data. Eight of the datasets used in the experiments were obtained on UCI and KEEL, and the other three datasets were created manually. The experimental results confirm that the model can effectively improve the classification accuracy of imbalanced data and can be used as a new imbalanced data classification method.

关键词： Support vector machines classification algorithms Optimization Machine learning algorithms Sampling methods Data models Search problems Imbalanced data machine learning sparrow search algorithm support vector machine oversampling

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Cascading Autoencoder With Attention Residual U-Net for Multi-Class Plant Leaf Disease Segmentation and classification

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IEEE ACCESS 2023年 11卷 98153-98170页

作者： Abinaya, S. Kumar, Kandagatla Uttej Alphonse, A. Sherly Vellore Inst Technol Sch Comp Sci & Engn Chennai 600127 India

Plant leaf diseases pose a significant threat to global food security and cause substantial economic losses. The objective of this study is to develop an effective approach for early detection and accurate identification of plant leaf diseases using computer vision techniques. The proposed method, Cascading Autoencoder with Attention Residual U-Net (CAAR-UNet), leverages deep learning to achieve precise segmentation and classification of plant leaf diseases. By cascading Symmetric Autoencoders with Attention Residual U-Net model and training on a custom dataset, it surpassed existing methods in identifying four disease classes. The model achieves remarkable accuracy, with a mean pixel accuracy of 95.26% and a weighted mean intersection over union of 0.7451, accurately capturing individual pixels and delineating disease class boundaries. This approach holds great potential in facilitating early plant disease detection and improving crop management practices. Its adoption can significantly impact food security worldwide, addressing a critical gap in the agricultural sector. The results highlight the effectiveness of the proposed strategy in plant disease management and open the door for further research in this field.

关键词： Image segmentation Plant diseases Deep learning Feature extraction Crops Decoding Transfer learning classification algorithms Encoding Plant disease semantic segmentation classification symmetric autoencoder attention residual U-Net

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Multisubject Analysis and classification of Books and Book Collections, Based on a Subject Term Vocabulary and the Latent Dirichlet Allocation

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IEEE ACCESS 2023年 11卷 120881-120898页

作者： Makris, Nikolaos Mitrou, Nikolaos Natl Tech Univ Athens Sch Elect & Comp Engn Athens 15780 Greece

In this paper, a new method for automatically analyzing and classifying books and book collections according to the subjects they cover is presented. It is based on a combination of the LDA method for discovering latent topics in the collection, on the one hand, and the description of subjects by means of a subject term vocabulary, on the other. Books, topics and subjects, all are modelled as bag-of-words, with specific distributions over the underlying word vocabulary. The Table of Contents(ToC) was used to describe the books, instead of their entire body, while subject (or standard) documents are produced by a subject term hierarchy of the respective disciplines. Frequency-of-terms in the documents and word-generative probabilistic models (as the ones postulated by LDA) were integrated into a consistent statistical framework. Using Bayesian statistics and simple marginalization equations we were able to transform the expressions of the books from distributions over unlabeled topics (derived by the LDA) to distributions over labeled subjects representing the respective disciplines (Physical sciences, Health sciences, Mathematics, etc). More specifically, the necessary theoretical basis is firstly established, with each subject formally defined by the respective branch of a subject term hierarchy (much like a ToC) or the respective bag of words (single words and biwords) produced by flattening the hierarchy branch;flattening is realized by taking all the terms of the nodes and leaves of the branch with repetitions allowed. Being confined within a closed set of subjects, we are able to invert the frequency-of-terms in each subject [also interpreted as the probability of generating a term (w(n)) when sampling the subject (s(i)) and denoted by Pr{w(n)|s(i)})] and express each term as a weighted mixture(or probability distribution) of subjects, denoted by Pr{si |wn}. This is the key idea of the proposed method. Then, any document (dm) can be expressed as a weighted mixt

关键词： Artificial intelligence digital libraries classification algorithms latent Dirichlet allocation multi-subject classification statistical natural language processing subject headings university coursebooks Springer ebooks Kallipos project

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classification of Cervical Precursor Lesions via Local Histogram and Cell Morphometric Features

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2023年第4期27卷 1747-1757页

作者： Calik, Nurullah Albayrak, Abdulkadir Akhan, Asl Turkmen, Ilknur Capar, Abdulkerim Toreyin, Behcet Ugur Bilgin, Gokhan Muezzinoglu, Bahar Durak-Ata, Lutfiye Istanbul Medeniyet Univ Dept Biomed Engn TR-34700 Istanbul Turkiye Dicle Univ Dept Comp Engn TR-21280 Diyarbakir Turkiye Selahaddin Eyyubi Hosp Dept Pathol TR-21100 Diyarbakir Turkiye Mem Sisli Hosp Dept Pathol TR-34360 Istanbul Turkiye Istanbul Tech Univ Informat Inst TR-34467 Istanbul Turkiye Yildiz Tech Univ YTU Dept Comp Engn TR-34220 Istanbul Turkiye Istanbul Medipol Univ Dept Pathol TR-34214 Istanbul Turkiye

Cervical squamous intra-epithelial lesions (SIL) are precursor cancer lesions and their diagnosis is important because patients have a chance to be cured before cancer develops. In the diagnosis of the disease, pathologists decide by considering the cell distribution from the basal to the upper membrane. The idea, inspired by the pathologists' point of view, is based on the fact that cell amounts differ in the basal, central, and upper regions of tissue according to the level of Cervical Intraepithelial Neoplasia (CIN). Therefore, histogram information can be used for tissue classification so that the model can be explainable. In this study, two different classification schemes are proposed to show that the local histogram is a useful feature for the classification of cervical tissues. The first classifier is Kullback Leibler divergence-based, and the second one is the classification of the histogram by combining the embedding feature vector from morphometric features. These algorithms have been tested on a public *** method we propose in the study achieved an accuracy performance of 78.69% in a data set where morphology-based methods were 69.07% and Convolutional Neural Network (CNN) patch-based algorithms were 75.77%. The proposed statistical features are robust for tackling real-life problems as they operate independently of the lesions manifold.

关键词： Lesions Image segmentation Histograms Feature extraction Convolutional neural networks Pathology classification algorithms Cervical lesions cervix hemotoxylen and eosin local histogram features cell morphometric features Kullback-Leibler divergence

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STDPboost: A Self-Training Method Based on Density Peaks and Improved Adaboost for Semi-Supervised classification

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IEEE ACCESS 2023年 11卷 72974-72989页

作者： Lin, Xu Li, Junnan Anhui Sanlian Univ Sch Comp Engn Hefei 230601 Anhui Peoples R China Chongqing Ind Polytech Coll Sch Artificial Intelligence & Big Data Chongqing 401120 Peoples R China

The self-training methods have been praised by extensive research in semi-supervised classification. Mislabeling is the main challenge in self-training methods. Multiple variations of self-training methods are recently proposed against mislabeling from the following one of two aspects: a) using heuristic rules to find high-confidence unlabeled samples that can easily be predicted correctly in each iteration;b) enhancing prediction performance by employing ensemble classifiers composed of multiple weak classifiers. Yet, they still suffer from the following issues: a);most strategies for finding high-confidence unlabeled samples heavily rely on parameters;b) almost all employed ensemble classifiers originally designed for supervised classifiers and may not be suitable for semi-supervised classification due to the limited number and unrepresented distribution of the initial labeled data;c) few can overcome mislabeling from the above two aspects at the same time. To advance the state of the art, a new self-training method based on density peaks clustering and improved Adaboost is presented and named as STDPboost. In the iterative self-taught process, a new density peaks clustering-based strategy is proposed to find high-confidence unlabeled samples and a new ensemble classifier named AdaboostSEMI and more suitable for semi-supervised classification is proposed to predict high-confidence unlabeled samples, which overcomes mislabeling and the mentioned shortcomings of existing self-training methods. Intensive experiments on benchmark data sets have proven that STDPboost outperforms 7 state-of-the-art self-training methods in average classification accuracy of KNN classifier and CART classifier with the percentages of the initial labeled data from 10% to 50% due to further alleviating mislabeling.

关键词： Training classification algorithms Prediction algorithms Iterative methods Forestry Clustering algorithms Predictive models Semisupervised learning Semi-supervised learning semi-supervised classification self-training methods oversampling techniques Adaboost

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Driver Behavior classification: A Systematic Literature Review

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IEEE ACCESS 2023年 11卷 14128-14153页

作者： Bouhsissin, Soukaina Sael, Nawal Benabbou, Faouzia Hassan II Univ Casablanca Fac Sci Ben MSick Lab Informat Technol & Modeling Casablanca 20000 Morocco

Driver behavior is receiving increasing attention because of the staggering number of road accidents. Many road safety reports regard human behavior as the most important factor in the likelihood of accidents. The detection and classification of aggressive or abnormal driver behavior is an essential requirement in the real world to avoid deadly road accidents and to protect road users. The automatic detection of a driver's behavior aids in the prevention of dangerous situations for the driver and all other participants in the driving environment, as well as the implementation of corrective measures. This paper presents a systematic literature review (SLR) of driver behavior classification. This study aimed to highlight and analyze the different types of driver behavior, types of studies, data sources, datasets, features, preprocessing techniques, and artificial intelligence algorithms used to classify driver behavior and its performance. Based on the results obtained from the analysis of the selected works, we aim to identify the key contributions and challenges of studying driver behavior classification and propose potential avenues for further directions for practitioners and researchers.

关键词： Intelligent vehicles Behavioral sciences Systematics classification algorithms Bibliographies Support vector machines Sensors Driver behavior intelligent transport system systematic literature review machine learning deep learning

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classification and Prediction of Communication Cables Length Based on S-Parameters Using a Machine-Learning Method

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IEEE ACCESS 2023年 11卷 108041-108049页

作者： Al Bataineh, Mohammad Umar, Malik Mohamed Moin, Aquib Hussein, Mousa I. Al Ahmad, Mahmoud United Arab Emirates Univ Elect & Commun Engn Dept Al Ain U Arab Emirates Yarmouk Univ Telecommun Engn Dept Irbid 21163 Jordan United Arab Emirates Univ Phys Dept Al Ain U Arab Emirates

The length of the communication cables is a significant indicator of signal integrity. The associated scattering parameters characteristics of a communication cable effectively enable its length estimation. This paper proposes a novel machine learning-based algorithm that utilizes Support Vector Machines (SVM) to predict and classify cable lengths. Specifically, the algorithm employs an SVM Regression Model (SVR) and an SVM classification Model (SVC) to predict and classify cable lengths based on their S-parameters (S-21 measurements). As the data under investigation are inseparable, linear, and high-dimensional, SVM has been implemented. The current approach was implemented to verify the length of two datasets, underground and overground cables, with different environmental conditions. The present research introduces an innovative machine-learning algorithm that employs an S-parameter-centric methodology to predict variations in communication cable lengths. Specifically, the SVR model achieved R-2 values of approximately 0.987 for underground cables and 0.991 for overground cables. Meanwhile, the SVC model demonstrated varying levels of accuracy, with optimal performance seen in five classes for underground cables and four classes for overground cables. The SVM model efficiently extracts and weighs features for high-accuracy predictions in nonlinear, multiclass scenarios, making it the optimal model for this work.

关键词： Power cables Support vector machines Scattering parameters Communication cables Predictive models Machine learning Static VAr compensators classification algorithms classification prediction cable length S-parameters machine-learning method

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