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A Pruning Optimized Fast Learn plus plus NSE algorithm

IEEE ACCESS 2021年 9卷 150733-150743页

作者： Chen, Yong Zhu, Yuquan Chen, Haifeng Shen, Yan Xu, Zhao Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 212013 Jiangsu Peoples R China China Unicom Internet Things Co Ltd Nanjing 210014 Jiangsu Peoples R China Jiangsu Univ Dept Informat Management & Informat Syst Zhenjiang 212013 Jiangsu Peoples R China Nanjing Longyuan Microelect Technol Co Ltd Nanjing 211106 Jiangsu Peoples R China

Due to the large number of typical applications, it is very important and urgent to study the fast classification learning of accumulated big data in nonstationary environments. The newly proposed algorithm, named Learn++.NSE, is one of the important research results in this research field. And a pruning version, named Learn++.NSE-Error-based, was given for accumulated big data to improve the learning efficiency. However, the studies have found that the Learn++.NSE-Error-based algorithm often encounters a situation that the newly generated base classifier is pruned in the next integration, which reduces the accuracy of the ensemble classifier. The newly generated base classifier is very important in the next ensemble learning and should be retained. Therefore, the two latest base classifiers are reserved without being pruned, and a new pruning algorithm named NewLearn++.NSE-Error-based was proposed. The experimental results on the generated dataset and the real-world dataset show that NewLearn++.NSE-Error-based can further improve the accuracy of the ensemble classifier under the premise of obtaining the same time complexity as Learn++.NSE algorithm. It is suitable for fast classification learning of long-term accumulated big data.

关键词： classification algorithms Big Data Error analysis Detection algorithms Data models Batch production systems Probability distribution Ensemble learning nonstationary environment classification algorithm big data mining

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Filtrování paketů v počítačových sítích

Filtrování paketů v počítačových sítích

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作者： Faron, Jan Brno University of Technology

Tato bakalářská práce se zabývá problematikou klasifikace paketů v počítačových sítích. V úvodu jsou popsány některé oblasti využívající klas... 详细信息

Tato bakalářská práce se zabývá problematikou klasifikace paketů v počítačových sítích. V úvodu jsou popsány některé oblasti využívající klasifikaci paketů. Dále je uvedena potřebná teorie spolu s požadavky na klasifikační algoritmus. Jsou popsány čtyři vysokoúrovňové přístupy ke klasifikaci paketů. Ke každému přístupu jsou popsány principy několika algoritmů. Pro detailnější rozbor a implementaci je vybrán algoritmus EffiCuts. Tento klasifikační algoritmus je porovnán s jinými klasifikačními algoritmy z knihovny NetBench.

关键词： klasifikace paketů efficuts klasifikační algoritmus packet classification efficuts classification algorithm Text

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T-DFNN: An Incremental Learning algorithm for Intrusion Detection Systems

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IEEE ACCESS 2021年 9卷 154156-154171页

作者： Data, Mahendra Aritsugi, Masayoshi Kumamoto Univ Grad Sch Sci & Technol Kumamoto 8608555 Japan Brawijaya Univ Dept Comp Sci Malang 65145 Indonesia Kumamoto Univ Fac Adv Sci & Technol Kumamoto 8608555 Japan

Machine learning has recently become a popular algorithm in building reliable intrusion detection systems (IDSs). However, most of the models are static and trained using datasets containing all targeted intrusions. If new intrusions emerge, these trained models must be retrained using old and new datasets to classify all intrusions accurately. In real-world situations, new threats continuously appear. Therefore, machine learning algorithms used for IDSs should have the ability to learn incrementally when these new intrusions emerge. To solve this issue, we propose T-DFNN. T-DFNN is an algorithm capable of learning new intrusions incrementally as they emerge. A T-DFNN model is composed of multiple deep feedforward neural network (DFNN) models connected in a tree-like structure. We examined our proposed algorithm using CICIDS2017, an open and widely used network intrusion dataset covering benign traffic and the most common network intrusions. The experimental results showed that the T-DFNN algorithm can incrementally learn new intrusions and reduce the catastrophic forgetting effect. The macro average of the F1-score of the T-DFNN model was over 0.85 for every retraining process. In addition, our proposed T-DFNN model has some advantages in several aspects compared to other models. Compared to the DFNN and Hoeffding tree models trained with a dataset containing only the latest targeted intrusions, our proposed T-DFNN model has higher F1-scores. Moreover, our proposed T-DFNN model has significantly shorter training times than a DFNN model trained using a dataset containing all targeted intrusions. Even though several factors can affect the duration of the training process, the T-DFNN algorithm shows promising results in solving the problem of ever-evolving network intrusion variants.

关键词： Machine learning algorithms Training Computational modeling Deep learning Machine learning Support vector machines Task analysis Network intrusion detection incremental learning catastrophic forgetting deep learning classification algorithm

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A new locally adaptive k-nearest neighbor algorithm based on discrimination class

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KNOWLEDGE-BASED SYSTEMS 2020年 204卷 106185-106185页

作者： Pan, Zhibin Wang, Yikun Pan, Yiwei Xi An Jiao Tong Univ Fac Elect & Informat Engn Sch Informat & Commun Engn Xian 710049 Shaanxi Peoples R China CAST Natl Key Lab Sci & Technol Space Microwave Xian Peoples R China

The k-nearest neighbor (kNN) rule is a classical non-parametric classification algorithm in pattern recognition, and has been widely used in many fields due to its simplicity, effectiveness and intuitiveness. However, the classification performance of the kNN algorithm suffers from the choice of a fixed and single value of k for all queries in the search stage and the use of simple majority voting rule in the decision stage. In this paper, we propose a new kNN-based algorithm, called locally adaptive k-nearest neighbor algorithm based on discrimination class (DC-LAKNN). In our method, the role of the second majority class in classification is for the first time considered. Firstly, the discrimination classes at different values of k are selected from the majority class and the second majority class in the k-neighborhood of the query. Then, the adaptive k value and the final classification result are obtained according to the quantity and distribution information on the neighbors in the discrimination classes at each value of k. Extensive experiments on eighteen real-world datasets from UCI (University of California, Irvine) Machine Learning Repository and KEEL (Knowledge Extraction based on Evolutionary Learning) Repository show that the DC-LAKNN algorithm achieves better classification performance compared to standard kNN algorithm and nine other state-of-the-art kNN-based algorithms. (C) 2020 Elsevier B.V. All rights reserved.

关键词： classification algorithm k-nearest neighbor rule Majority class Second majority class Discrimination class Adaptive k value

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ADAMS Simulation and HHT Feature Extraction Method for Bearing Faults of Coal Shearer

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PROCESSES 2024年第1期12卷 164页

作者： Qin, Yi-Fan Fu, Xiang Li, Xiao-Kun Li, Hao-Jie Taiyuan Univ Technol Coll Min Engn Taiyuan 030024 Peoples R China Shanxi Coking Coal Grp Co Ltd Postdoctoral Workstat Taiyuan 030024 Peoples R China State Key Lab Intelligent Min Equipment Technol Taiyuan 030024 Peoples R China

Aiming at the problem of difficult fault diagnosis work caused by the difficulty of data acquisition of the bearing in the traction part of a coal mining machine, a method of ADAMS simulation and HHT feature extraction of the bearing fault of a coal mining machine is proposed. First of all, take the traction section bearing as the research object, use the virtual prototype in the establishment of the healthy state of coal mining machine traction section model based on the establishment of the bearing inner ring fault, rolling body fault, outer ring fault of the coal mining machine traction section dynamics model, and then after the EMD decomposition, each IMF component of the Hilbert transform, to obtain the signal in the time-frequency plane of the time-frequency joint characteristics, to get the HHT marginal spectra and to different Under different working conditions, the bearing vibration signal features are mined by quantitative feature extraction. Finally, a variety of mainstream machine learning algorithms are introduced to classify the features, and the results show that the feature extraction method in this paper is universal and provides valuable theoretical support and technical guidance for the field application of coal mining machine-bearing fault diagnosis.

关键词： coal mining machine bearing fault diagnosis feature extraction machine learning ADAMS empirical modal decomposition classification algorithm Hilbert-Huang transform (HHT)

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What Are Sheep Doing? Tri-Axial Accelerometer Sensor Data Identify the Diel Activity Pattern of Ewe Lambs on Pasture

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SENSORS 2021年第20期21卷 6816页

作者： Ikurior, Seer J. Marquetoux, Nelly Leu, Stephan T. Corner-Thomas, Rene A. Scott, Ian Pomroy, William E. Massey Univ Sch Vet Sci Palmerston North 4410 New Zealand Univ Agr Coll Vet Med Makurdi 970231 Nigeria Massey Univ Sch Vet Sci EpiCtr Palmerston North 4410 New Zealand Univ Adelaide Sch Anim & Vet Sci Roseworthy 5371 Australia Massey Univ Sch Agr & Environm Palmerston North 4410 New Zealand

Monitoring activity patterns of animals offers the opportunity to assess individual health and welfare in support of precision livestock farming. The purpose of this study was to use a triaxial accelerometer sensor to determine the diel activity of sheep on pasture. Six Perendale ewe lambs, each fitted with a neck collar mounting a triaxial accelerometer, were filmed during targeted periods of sheep activities: grazing, lying, walking, and standing. The corresponding acceleration data were fitted using a Random Forest algorithm to classify activity (=classifier). This classifier was then applied to accelerometer data from an additional 10 ewe lambs to determine their activity budgets. Each of these was fitted with a neck collar mounting an accelerometer as well as two additional accelerometers placed on a head halter and a body harness over the shoulders of the animal. These were monitored continuously for three days. A classification accuracy of 89.6% was achieved for the grazing, walking and resting activities (i.e., a new class combining lying and standing activity). Triaxial accelerometer data showed that sheep spent 64% (95% CI 55% to 74%) of daylight time grazing, with grazing at night reduced to 14% (95% CI 8% to 20%). Similar activity budgets were achieved from the halter mounted sensors, but not those on a body harness. These results are consistent with previous studies directly observing daily activity of pasture-based sheep and can be applied in a variety of contexts to investigate animal health and welfare metrics e.g., to better understand the impact that young sheep can suffer when carrying even modest burdens of parasitic nematodes.

关键词： sheep diel activity classification algorithm tri-axial accelerometers health monitoring

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Method for In-Operando Contamination of Lithium Ion Batteries for Prediction of Impurity-Induced Non-Obvious Cell Damage

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BATTERIES-BASEL 2022年第4期8卷 35页

作者： Hoeschele, Patrick Heindl, Simon Franz Schneider, Bernd Sinz, Wolfgang Ellersdorfer, Christian Graz Univ Technol Vehicle Safety Inst Inffeldgasse 23-1 A-8010 Graz Austria

The safety of lithium-ion batteries within electrified vehicles plays an important role. Hazards can arise from contaminated batteries resulting from non-obvious damages or insufficient production processes. A systematic examination requires experimental methods to provoke a defined contamination. Two prerequisites were required: First, the extent and type of contamination should be determinable to exclude randomness. Second, specimens should work properly before the contamination, enabling realistic behavior. In this study, two experimental methods were developed to allow for the first time a controlled and reproducible application of water or oxygen into 11 single-layer full cells (Li4Ti5O12/LiCoO2) used as specimens during electrical cycling. Electrochemical impedance spectroscopy was used to continuously monitor the specimens and to fit the parameters of an equivalent circuit model (ECM). For the first time, these parameters were used to calibrate a machine-learning algorithm which was able to predict the contamination state. A decision tree was calibrated with the ECM parameters of eight specimens (training data) and was validated by predicting the contamination state of the three remaining specimens (test data). The prediction quality proved the usability of classification algorithms to monitor for contaminations or non-obvious battery damage after manufacturing and during use. It can be an integral part of battery management systems that increases vehicle safety.

关键词： lithium-ion battery safety in operando contamination electrochemical impedance spectroscopy equivalent circuit model machine learning classification algorithm decision tree

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ARS k NN-A k -NN Classifier Using Mass Based Similarity Measure

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Procedia Computer Science 2015年 46卷 457-462页

作者： Ashish Kumar Roheet Bhatnagar Sumit Srivastava Manipal University Jaipur Dehmi Kalan Jaipur 303007 India

k -Nearest Neighbor ( k -NN) classification technique is one of the most elementary and straightforward classification methods. Although distance learning is in the core of k -NN classification, similarity can be preferred upon distance in several practical applications. This paper proposes a novel algorithm for learning a class of an instance based on a similarity measure which does not calculate distance, for k -Nearest Neighbor ( k -NN) classification.

关键词： classification algorithm Data Mining Nearest Neighbor Similarity Measures

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Algoritmy pro klasifikaci paketů

Algoritmy pro klasifikaci paketů

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作者： Myška, Michal Brno University of Technology

Tato bakalářská práce se zabývá problematikou klasifikace paketů v počítačových sítích. V úvodu je vysvětlena teorie popisující počítačové sí... 详细信息

Tato bakalářská práce se zabývá problematikou klasifikace paketů v počítačových sítích. V úvodu je vysvětlena teorie popisující počítačové sítě a přístupy ke klasifikaci. Dále je vybrán klasifikační algoritmus založený na tvorbě rozhodovacího stromu. Důležitá vlastnost tohoto algoritmu je možnost parametrizace. V práci je analyzován vliv jednotlivých parametrů na tvorbu rozhodovacího stromu. Vybraný klasifikační algoritmus je také porovnán s jinými klasifikačními algoritmy z experimentální knihovny Netbench.

关键词： Klasifikace paketů filtrování paketů HiCuts klasifikační algoritmus Packet classification packet filters HiCuts classification algorithm Text

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Gaussian prior based adaptive synthetic sampling with non-linear sample space for imbalanced learning

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KNOWLEDGE-BASED SYSTEMS 2020年 191卷 105231-105231页

作者： Zhang, Tianlun Li, Yang Wang, Xizhao Dalian Maritime Univ Coll Informat Sci & Technol Dalian 116026 Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China

In the presence of skewed category distribution, most learning algorithms fail to provide favorable performance on the representation about data characteristics. Thus learning from imbalanced data is a crucial challenge in the field of data engineering and knowledge discovery. In this work, we proposed an imbalanced learning method to generate minority samples for the compensation of class distribution skews. Different from existing synthetic over-sampling techniques, the data generation is conducted within the hyperplane rather than on the hyperline, thus the proposed method breaks down the ties imposed by the linear interpolation. In addition, this proposed method minimizes the sampling uncertain and risk by integrating a prior knowledge about the minority class instances. Moreover, a multi-objective optimization combined with error bound model develops this proposed method into an adaptive imbalanced learning. Extensive experiments have been performed on imbalanced issues, and the experimental results demonstrate that this method can improve the performance of different classification algorithms. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Imbalanced learning Error bound model Adaptive method classification algorithm Gaussian mixture model

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