scholarly journals Patient classification as an outlier detection problem: An application of the One-Class Support Vector Machine

NeuroImage ◽  
2011 ◽  
Vol 58 (3) ◽  
pp. 793-804 ◽  
Author(s):  
Janaina Mourão-Miranda ◽  
David R. Hardoon ◽  
Tim Hahn ◽  
Andre F. Marquand ◽  
Steve C.R. Williams ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Outlier Detection ◽  
Support Vector ◽  
Patient Classification ◽  
Detection Problem ◽  
The One

scholarly journals Short-Term Traffic Flow Forecasting Based on Data-Driven Model

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 152 ◽  
Author(s):  
Su-qi Zhang ◽  
Kuo-Ping Lin
Keyword(s):  
Support Vector Machine ◽  
Traffic Flow ◽  
Extreme Learning Machine ◽  
Support Vector ◽  
Local Optimum ◽  
Short Term ◽  
Swarm Optimization ◽  
Traffic Flow Forecasting ◽  
Learning Machine ◽  
The One

Short-term traffic flow forecasting is the technical basis of the intelligent transportation system (ITS). Higher precision, short-term traffic flow forecasting plays an important role in alleviating road congestion and improving traffic management efficiency. In order to improve the accuracy of short-term traffic flow forecasting, an improved bird swarm optimizer (IBSA) is used to optimize the random parameters of the extreme learning machine (ELM). In addition, the improved bird swarm optimization extreme learning machine (IBSAELM) model is established to predict short-term traffic flow. The main researches in this paper are as follows: (1) The bird swarm optimizer (BSA) is prone to fall into the local optimum, so the distribution mechanism of the BSA optimizer is improved. The first five percent of the particles with better fitness values are selected as producers. The last ten percent of the particles with worse fitness values are selected as beggars. (2) The one-day and two-day traffic flows are predicted by the support vector machine (SVM), particle swarm optimization support vector machine (PSOSVM), bird swarm optimization extreme learning machine (BSAELM) and IBSAELM models, respectively. (3) The prediction results of the models are evaluated. For the one-day traffic flow sequence, the mean absolute percentage error (MAPE) values of the IBSAELM model are smaller than the SVM, PSOSVM and BSAELM models, respectively. The experimental analysis results show that the IBSAELM model proposed in this study can meet the actual engineering requirements.

scholarly journals Fast outlier detection for high-dimensional data of wireless sensor networks

2020 ◽  
Vol 16 (10) ◽  
pp. 155014772096383
Author(s):  
Yan Qiao ◽  
Xinhong Cui ◽  
Peng Jin ◽  
Wu Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Support Vector Machine ◽  
Sensor Networks ◽  
Outlier Detection ◽  
Large Scale ◽  
Deep Belief Network ◽  
Wireless Sensor ◽  
High Dimensional ◽  
Support Vector ◽  
Belief Network

This article addresses the problem of outlier detection for wireless sensor networks. As increasing amounts of observational data are tending to be high-dimensional and large scale, it is becoming increasingly difficult for existing techniques to perform outlier detection accurately and efficiently. Although dimensionality reduction tools (such as deep belief network) have been utilized to compress the high-dimensional data to support outlier detection, these methods may not achieve the desired performance due to the special distribution of the compressed data. Furthermore, because most existed classification methods must solve a quadratic optimization problem in their training stage, they cannot perform well in large-scale datasets. In this article, we developed a new form of classification model called “deep belief network online quarter-sphere support vector machine,” which combines deep belief network with online quarter-sphere one-class support vector machine. Based on this model, we first propose a model training method that learns the radius of the quarter sphere by a sorting method. Then, an online testing method is proposed to perform online outlier detection without supervision. Finally, we compare the proposed method with the state of the arts using extensive experiments. The experimental results show that our method not only reduces the computational cost by three orders of magnitude but also improves the detection accuracy by 3%–5%.

CLASSIFICATION OF MULTIPLE CANCER TYPES USING FUZZY SUPPORT VECTOR MACHINES AND OUTLIER DETECTION METHODS

2005 ◽  
Vol 17 (06) ◽  
pp. 300-308 ◽  
Author(s):  
LI-YEH CHUANG ◽  
CHENG-HONG YANG ◽  
LI-CHENG JIN
Keyword(s):  
Support Vector Machine ◽  
Outlier Detection ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Detection Methods ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Multiple Cancer ◽  
Flexible Architecture ◽  
Cancer Types

The support vector machine (SVM) is a new learning method and has shown comparable or better results than the neural networks on some applications. In this paper, we applied SVM to classify multiple cancer types by gene expression profiles and exploit some strategies of the SVM method, including fuzzy logic and statistical theories. Using the proposed strategies and outlier detection methods, the FSVM (fuzzy support vector machine) can achieve a comparable or better performance than other methods, and provide a more flexible architecture to discriminate against SRBCT and non-SRBCT samples.

Outlier Detection for Observational Data of Automatic Meteorological Station Based on Least Square Support Vector Machine

2013 ◽  
Vol 295-298 ◽  
pp. 945-949
Author(s):  
Zhen Meng ◽  
Shi Chang Zhang ◽  
Zeng Lin Huang
Keyword(s):  
Support Vector Machine ◽  
Observational Data ◽  
Outlier Detection ◽  
Meteorological Data ◽  
Meteorological Factor ◽  
Meteorological Station ◽  
Temperature Data ◽  
Least Square ◽  
Support Vector ◽  
Svm Model

Automatic meteorological station is important for meteorological observation and the existence of outliers in the observational data is inevitable. The paper proposes outlier detection for observational data of automatic meteorological station based on least square support vector machine (LS-SVM). The method establishes the LS-SVM model for the meteorological factor and uses the model to evaluate the observational data. If the observational data deviate from the model, they would be seemed as outliers. The ground temperature data observed by two real automatic meteorological stations are used in experiments. Experiments results verify that the proposed method realize outlier detection for observational data of automatic meteorological station effectively and ensures subsequent process and analysis of the meteorological data.

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