scholarly journals Fast Incremental SVDD Learning Algorithm with the Gaussian Kernel

2019 ◽  
Vol 33 ◽  
pp. 3991-3998 ◽  
Author(s):  
Hansi Jiang ◽  
Haoyu Wang ◽  
Wenhao Hu ◽  
Deovrat Kakde ◽  
Arin Chaudhuri
Keyword(s):  
Outlier Detection ◽  
Learning Algorithm ◽  
Large Data ◽  
Real Data ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Detection Accuracy ◽  
Single Class ◽  
Support Vectors

Support vector data description (SVDD) is a machine learning technique that is used for single-class classification and outlier detection. The idea of SVDD is to find a set of support vectors that defines a boundary around data. When dealing with online or large data, existing batch SVDD methods have to be rerun in each iteration. We propose an incremental learning algorithm for SVDD that uses the Gaussian kernel. This algorithm builds on the observation that all support vectors on the boundary have the same distance to the center of sphere in a higher-dimensional feature space as mapped by the Gaussian kernel function. Each iteration involves only the existing support vectors and the new data point. Moreover, the algorithm is based solely on matrix manipulations; the support vectors and their corresponding Lagrange multiplier αi’s are automatically selected and determined in each iteration. It can be seen that the complexity of our algorithm in each iteration is only O(k2), where k is the number of support vectors. Experimental results on some real data sets indicate that FISVDD demonstrates significant gains in efficiency with almost no loss in either outlier detection accuracy or objective function value.

Helicopter main reduction planetary gear fault diagnosis method based on SVDD

2020 ◽  
Vol 64 (1-4) ◽  
pp. 137-145
Author(s):  
Yubin Xia ◽  
Dakai Liang ◽  
Guo Zheng ◽  
Jingling Wang ◽  
Jie Zeng
Keyword(s):  
Fault Diagnosis ◽  
Planetary Gear ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Energy Characteristics ◽  
Gear Fault ◽  
Channel Information ◽  
Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

scholarly journals Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
S. Ganapathy ◽  
P. Yogesh ◽  
A. Kannan
Keyword(s):  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Outlier Detection ◽  
Intelligent Agent ◽  
Support Vector ◽  
Detection Accuracy ◽  
Data Set ◽  
Agent Based ◽  
Multiclass Svm

Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set.

scholarly journals Cascade Support Vector Machines with Dimensionality Reduction

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Oliver Kramer
Keyword(s):  
Support Vector Machines ◽  
Dimensionality Reduction ◽  
Principal Component ◽  
Large Data ◽  
Locally Linear Embedding ◽  
Benchmark Problems ◽  
Support Vector ◽  
Training Set ◽  
Support Vectors ◽  
Vector Machines

Cascade support vector machines have been introduced as extension of classic support vector machines that allow a fast training on large data sets. In this work, we combine cascade support vector machines with dimensionality reduction based preprocessing. The cascade principle allows fast learning based on the division of the training set into subsets and the union of cascade learning results based on support vectors in each cascade level. The combination with dimensionality reduction as preprocessing results in a significant speedup, often without loss of classifier accuracies, while considering the high-dimensional pendants of the low-dimensional support vectors in each new cascade level. We analyze and compare various instantiations of dimensionality reduction preprocessing and cascade SVMs with principal component analysis, locally linear embedding, and isometric mapping. The experimental analysis on various artificial and real-world benchmark problems includes various cascade specific parameters like intermediate training set sizes and dimensionalities.

scholarly journals Outlier Detection Using Improved Support Vector Data Description in Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4712
Author(s):  
Pei Shi ◽  
Guanghui Li ◽  
Yongming Yuan ◽  
Liang Kuang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Relative Density ◽  
Outlier Detection ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description

Wireless sensor networks (WSNs) are susceptible to faults in sensor data. Outlier detection is crucial for ensuring the quality of data analysis in WSNs. This paper proposes a novel improved support vector data description method (ID-SVDD) to effectively detect outliers of sensor data. ID-SVDD utilizes the density distribution of data to compensate SVDD. The Parzen-window algorithm is applied to calculate the relative density for each data point in a data set. Meanwhile, we use Mahalanobis distance (MD) to improve the Gaussian function in Parzen-window density estimation. Through combining new relative density weight with SVDD, this approach can efficiently map the data points from sparse space to high-density space. In order to assess the outlier detection performance, the ID-SVDD algorithm was implemented on several datasets. The experimental results demonstrated that ID-SVDD achieved high performance, and could be applied in real water quality monitoring.

scholarly journals Combustible Gas Classification Modeling using Support Vector Machine and Pairing Plot Scheme

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5018 ◽  
Author(s):  
Kyu-Won Jang ◽  
Jong-Hyeok Choi ◽  
Ji-Hoon Jeon ◽  
Hyun-Seok Kim
Keyword(s):  
Support Vector Machine ◽  
Gas Sensors ◽  
Learning Algorithm ◽  
Support Vector ◽  
Detection Accuracy ◽  
Minimum Concentration ◽  
Industrial Sites ◽  
Combustible Gas ◽  
Proposed Model ◽  
Combustible Gases

Combustible gases, such as CH4 and CO, directly or indirectly affect the human body. Thus, leakage detection of combustible gases is essential for various industrial sites and daily life. Many types of gas sensors are used to identify these combustible gases, but since gas sensors generally have low selectivity among gases, coupling issues often arise which adversely affect gas detection accuracy. To solve this problem, we built a decoupling algorithm with different gas sensors using a machine learning algorithm. Commercially available semiconductor sensors were employed to detect CH4 and CO, and then support vector machine (SVM) applied as a supervised learning algorithm for gas classification. We also introduced a pairing plot scheme to more effectively classify gas type. The proposed model classified CH4 and CO gases 100% correctly at all levels above the minimum concentration the gas sensors could detect. Consequently, SVM with pairing plot is a memory efficient and promising method for more accurate gas classification.

scholarly journals Outlier Detection in Wireless Sensor Networks Using Model Selection-Based Support Vector Data Descriptions

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4328 ◽  
Author(s):  
Zhan Huan ◽  
Chang Wei ◽  
Guang-Hui Li
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Model Selection ◽  
Outlier Detection ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Quality Data ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data

Wireless sensor networks (WSNs) are often deployed in harsh and unattended environments, which may cause the generation of abnormal or low quality data. The inaccurate and unreliable sensor data may increase generation of false alarms and erroneous decisions, so it’s very important to detect outliers in sensor data efficiently and accurately to ensure sound scientific decision-making. In this paper, an outlier detection algorithm (TSVDD) using model selection-based support vector data description (SVDD) is proposed. Firstly, the Toeplitz matrix random feature mapping is used to reduce the time and space complexity of outlier detection. Secondly, a novel model selection strategy is realized to keep the algorithm stable under the low feature dimensions, this strategy can select a relatively optimal decision model and avoid both under-fitting and overfitting phenomena. The simulation results on SensorScope and IBRL datasets demonstrate that, TSVDD achieves higher accuracy and lower time complexity for outlier detection in WSNs compared with existing methods.

scholarly journals Infrared–ultrasonic sensor fusion for support vector machine–based fall detection

2018 ◽  
Vol 29 (9) ◽  
pp. 2027-2039 ◽  
Author(s):  
Zhangjie Chen ◽  
Ya Wang
Keyword(s):  
Support Vector Machine ◽  
Sensor Fusion ◽  
Learning Algorithm ◽  
Fall Detection ◽  
Ultrasonic Sensor ◽  
Support Vector ◽  
Detection Accuracy ◽  
Data Recording ◽  
Fusion Algorithm ◽  
Average Accuracy

This article presents an infrared–ultrasonic sensor fusion approach for support vector machine–based fall detection, often required by elderly healthcare. Its detection algorithms and performance evaluation are detailed. The location, size, and temperature profile of the user can be estimated based on a novel sensory fusion algorithm. Different feature sets of the support vector machine–based machine learning algorithm are analyzed and their impact on fall detection accuracy is evaluated and compared empirically. Experiments study three non-fall activities, standing, sitting, and stooping, and two fall actions, forward falling and sideway falling, to simulate daily activities of the elderly. Fall detection accuracy studies are performed based on discretely and continuously (closer to reality) recorded experimental data, respectively. For the discrete data recording, an average accuracy of 92.2% is achieved when the stand-alone Grid-EYE is used and the accuracy is increased to 96.7% when sensor fusion is used. For the continuous data recording (180 training sets, 60 test sets at each distance), an average accuracy less than 70.0% is achieved when the stand-alone Grid-EYE is used and the accuracy is increased to around 90.3% after sensor fusion. New features will be explored in the next step to further increase detection accuracy.

scholarly journals Single-Class Data Descriptors for Mapping Panax notoginseng through P-Learning

2018 ◽  
Vol 8 (9) ◽  
pp. 1448 ◽  
Author(s):  
Fei Deng ◽  
Shengliang Pu
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Land Cover ◽  
Panax Notoginseng ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Density Estimator ◽  
Single Class ◽  
Target Distribution ◽  
Unit Class

Machine learning-based remote-sensing techniques have been widely used for the production of specific land cover maps at a fine scale. P-learning is a collection of machine learning techniques for training the class descriptors on the positive samples only. Panax notoginseng is a rare medicinal plant, which also has been a highly regarded traditional Chinese medicine resource in China for hundreds of years. Until now, Panax notoginseng has scarcely been observed and monitored from space. Remote sensing of natural resources provides us new insights into the resource inventory of Chinese materia medica resources, particularly of Panax notoginseng. Generally, land-cover mapping involves focusing on a number of landscape classes. However, sometimes a subset or one of the classes will be the only part of interest. In term of this study, the Panax notoginseng field is the right unit class. Such a situation makes single-class data descriptors (SCDDs) especially significant for specific land-cover interpretation. In this paper, we delineated the application such that a stack of SCDDs were trained for remote-sensing mapping of Panax notoginseng fields through P-learning. We employed and compared SCDDs, i.e., the simple Gaussian target distribution, the robust Gaussian target distribution, the minimum covariance determinant Gaussian, the mixture of Gaussian, the auto-encoder neural network, the k-means clustering, the self-organizing map, the minimum spanning tree, the k-nearest neighbor, the incremental support vector data description, the Parzen density estimator, and the principal component analysis; as well as three ensemble classifiers, i.e., the mean, median, and voting combiners. Experiments demonstrate that most SCDDs could achieve promising classification performance. Furthermore, this work utilized a set of the elaborate samples manually collected at a pixel-level by experts, which was intended to be a benchmark dataset for the future work. The measuring performance of SCDDs gives us challenging insights to define the selection criteria and scoring proof for choosing a fine SCDD in mapping a specific landscape class. With the increment of remotely sensed satellite data of the study area, the spatial distribution of Panax notoginseng could be continuously derived in the local area on the basis of SCDDs.

Automatic Recognition of Porcine Abnormalities Based on a Sound Detection and Recognition System

2019 ◽  
Vol 62 (6) ◽  
pp. 1755-1765
Author(s):  
Sunan Zhang ◽  
Jianyan Tian ◽  
Amit Banerjee ◽  
Jiangli Li
Keyword(s):  
Large Scale ◽  
Rapid Development ◽  
Recognition System ◽  
Automatic Recognition ◽  
Farm Animals ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Detection Accuracy ◽  
Sound Detection ◽  
Detection And Recognition

Abstract. With the rapid development of large-scale breeding, manual long-term monitoring of the daily activities and health of livestock is costly and time-consuming. Therefore, the application of bio-acoustics to automatic monitoring has received increasing attention. Although bio-acoustical techniques have been applied to the recognition of animal sounds in many studies, there is a dearth of studies on the automatic recognition of abnormal sounds from farm animals. In this study, an automatic detection and recognition system based on bio-acoustics is proposed to hierarchically recognize abnormal animal states in a large-scale pig breeding environment. In this system, we extracted the mel-frequency cepstral coefficients (MFCC) and subband spectrum centroid (SSC) as composite feature parameters. At the first level, support vector data description (SVDD) is used to detect abnormal sounds in the acoustic data. At the second level, a back-propagation neural network (BPNN) is used to classify five kinds of abnormal sounds in pigs. Furthermore, improved spectral subtraction is developed to reduce the noise interference as much as possible. Experimental results show that the average detection accuracy and the average recognition accuracy of the proposed system are 94.2% and 95.4%, respectively. The effectiveness of the proposed sound detection and recognition system was also verified through tests at a pig farm. Keywords: Abnormal sounds, MFCC, SSC, States of pigs, SVDD.

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