scholarly journals A Parameter-Free Outlier Detection Algorithm Based on Dataset Optimization Method

Information ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 26
Author(s):  
Liying Wang ◽  
Lei Shi ◽  
Liancheng Xu ◽  
Peiyu Liu ◽  
Lindong Zhang ◽  
...  
Keyword(s):  
Outlier Detection ◽  
Detection Method ◽  
Computational Cost ◽  
Optimization Method ◽  
Detection Algorithm ◽  
Threshold Function ◽  
Detection Algorithms ◽  
Original Dataset ◽  
Data Clusters ◽  
High Computational Cost

Recently, outlier detection has widespread applications in different areas. The task is to identify outliers in the dataset and extract potential information. The existing outlier detection algorithms mainly do not solve the problems of parameter selection and high computational cost, which leaves enough room for further improvements. To solve the above problems, our paper proposes a parameter-free outlier detection algorithm based on dataset optimization method. Firstly, we propose a dataset optimization method (DOM), which initializes the original dataset in which density is greater than a specific threshold. In this method, we propose the concepts of partition function (P) and threshold function (T). Secondly, we establish a parameter-free outlier detection method. Similarly, we propose the concept of the number of residual neighbors, as the number of residual neighbors and the size of data clusters are used as the basis of outlier detection to obtain a more accurate outlier set. Finally, extensive experiments are carried out on a variety of datasets and experimental results show that our method performs well in terms of the efficiency of outlier detection and time complexity.

A Community Detection Method Based on Multi-Objective Optimization Method

2014 ◽  
Vol 571-572 ◽  
pp. 177-182 ◽  
Author(s):  
Lu Wang ◽  
Yong Quan Liang ◽  
Qi Jia Tian ◽  
Jie Yang ◽  
Chao Song ◽  
...  
Keyword(s):  
Community Detection ◽  
Detection Method ◽  
Optimization Method ◽  
Research Area ◽  
Multi Objective ◽  
Detection Algorithms ◽  
Multi Objective Genetic Algorithm ◽  
Community Fitness ◽  
Active Research ◽  
Active Research Area

Community detection in complex network has been an active research area in data mining and machine learning. This paper proposed a community detection method based on multi-objective evolutionary algorithm, named CDMOEA, which tries to find the Pareto front by maximize two objectives, community score and community fitness. Fast and Elitist Multi-objective Genetic Algorithm is used to attained a set of optimal solutions, and then use Modularity function to choose the best one from them. The locus based adjacency representation is used to realize genetic representation, which ensures the effective connections of the nodes in the network during the process of population Initialization and other genetic operator. Uniform crossover is introduced to ensure population’s diversity. We compared it with some popular community detection algorithms in computer generated network and real world networks. Experiment results show that it is more efficient in community detection.

An Efficient Method to Detect Outliers in High Dimensional Data

2019 ◽  
Vol 16 (9) ◽  
pp. 3938-3944
Author(s):  
Atul Garg ◽  
Kamaljeet Kaur
Keyword(s):  
Outlier Detection ◽  
High Dimensional Data ◽  
Detection Algorithm ◽  
High Dimensional ◽  
The Novel ◽  
Linear Discriminant ◽  
Detection Techniques ◽  
Detection Algorithms ◽  
Large Sets ◽  
Detection Of Outliers

In this era, detection of outliers or anomalies from high dimensional data is really a great challenge. Normal data is distinguished from data containing anomalies using Outlier detection techniques which classifies new data as normal or abnormal. Different Outlier Detection algorithms are proposed by many researchers for high dimensional data and each algorithm has its own benefits and limitations. In the literature the researchers proposed different algorithms. For this work few algorithms such as Dice-Coefficient Index (DCI), Mapreduce Function and Linear Discriminant Analysis Algorithm (LDA) are considered. Mapreduce function is used to overcome the problem of large datasets. LDA is basically used in the reduction of the data dimensionality. In the present work a novel Hybrid Outlier Detection Algorithm (HbODA) is proposed for efficiently detection of outliers in high dimensional data. The important parameters efficiency, accuracy, computation cost, precision, recall etc. are focused for analyzing the performance of the novel hybrid algorithm. Experimental results on real large sets show that the proposed algorithm is better in detecting outliers than other traditional methods.

scholarly journals A Vertex Concavity-Convexity Detection Method for Three-Dimensional Spatial Objects Based on Geometric Algebra

2020 ◽  
Vol 9 (1) ◽  
pp. 25
Author(s):  
Pengcheng Yin ◽  
Jiyi Zhang ◽  
Xiying Sun ◽  
Di Hu ◽  
Zhifeng Shi ◽  
...  
Keyword(s):  
Geometric Algebra ◽  
Detection Method ◽  
Three Dimensional ◽  
Detection Algorithm ◽  
Basic Algorithm ◽  
Outer Product ◽  
Spatial Objects ◽  
Detection Algorithms ◽  
Product Operation ◽  
Object Based

Vertex concavity-convexity detection for spatial objects is a basic algorithm of computer graphics, as well as the foundation for the implementation of other graphics algorithms. In recent years, the importance of the vertex concavity-convexity detection algorithm for three-dimensional (3D) spatial objects has been increasingly highlighted, with the development of 3D modeling, artificial intelligence, and other graphics technologies. Nonetheless, the currently available vertex concavity-convexity detection algorithms mostly use two-dimensional (2D) polygons, with limited research on vertex concavity-convexity detection algorithms for 3D polyhedrons. This study investigates the correlation between the outer product and the topology of the spatial object based on the unique characteristic that the outer product operation in the geometric algebra has unified and definitive geometric implications in space, and with varied dimensionality. Moreover, a multi-dimensional unified vertex concavity-convexity detection algorithm framework for spatial objects is proposed, and this framework is capable of detecting vertex concavity-convexity for both 2D simple polygons and 3D simple polyhedrons.

An Effective Method for Defects Detection in Radiographic Images of Welds Based on Edge Detection and Morphology

2013 ◽  
Vol 290 ◽  
pp. 71-77
Author(s):  
Wen Ming Guo ◽  
Yan Qin Chen
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Detection Algorithm ◽  
Morphological Processing ◽  
Radiographic Images ◽  
X Ray ◽  
Weld Defects ◽  
Detection Algorithms ◽  
Detection Quality

In the current industrial production, as steel weld X-ray images are low contrasted and noisy, the efficiency and precision can’t be both ensured. This paper has studied three different edge detection algorithms and found the most suitable one to detect weld defects. Combined with this edge detection algorithm, we proposed a new weld defects detection method. This method uses defect features to find the defects in edge images with morphological processing. Compared to the traditional methods, the method has ensured detection quality of weld defects detection.

scholarly journals A New Semantic-Based Tool Detection Method for Robots

2021 ◽  
Vol 16 (2) ◽  
Author(s):  
Wenbai Chen ◽  
Chao He ◽  
Chen W.Z. ◽  
Chen Q.L. ◽  
Wu P.L.
Keyword(s):  
Detection Method ◽  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Detection Algorithms ◽  
Recognition Ability ◽  
Home Helper ◽  
Point Detection ◽  
Functional Components ◽  
Instance Segmentation ◽  
Better Than

Home helper robots have become more acceptable due to their excellent image recognition ability. However, some common household tools remain challenging to recognize, classify, and use by robots. We designed a detection method for the functional components of common household tools based on the mask regional convolutional neural network (Mask-R-CNN). This method is a multitask branching target detection algorithm that includes tool classification, target box regression, and semantic segmentation. It provides accurate recognition of the functional components of tools. The method is compared with existing algorithms on the dataset UMD Part Affordance dataset and exhibits effective instance segmentation and key point detection, with higher accuracy and robustness than two traditional algorithms. The proposed method helps the robot understand and use household tools better than traditional object detection algorithms.

scholarly journals Dynamic Object Detection Algorithm Based on Lightweight Shared Feature Pyramid

2021 ◽  
Vol 13 (22) ◽  
pp. 4610
Author(s):  
Li Zhu ◽  
Zihao Xie ◽  
Jing Luo ◽  
Yuhang Qi ◽  
Liman Liu ◽  
...  
Keyword(s):  
Object Detection ◽  
Feature Fusion ◽  
Computational Cost ◽  
Detection Algorithm ◽  
Dynamic Object ◽  
Shared Feature ◽  
Detection Model ◽  
Detection Algorithms ◽  
Adaptive Inference ◽  
Feature Pyramid

Current object detection algorithms perform inference on all samples at a fixed computational cost in the inference stage, which wastes computing resources and is not flexible. To solve this problem, a dynamic object detection algorithm based on a lightweight shared feature pyramid is proposed, which performs adaptive inference according to computing resources and the difficulty of samples, greatly improving the efficiency of inference. Specifically, a lightweight shared feature pyramid network and lightweight detection head is proposed to reduce the amount of computation and parameters in the feature fusion part and detection head of the dynamic object detection model. On the PASCAL VOC dataset, under the two conditions of “anytime prediction” and “budgeted batch object detection”, the performance, computation amount and parameter amount are better than the dynamic object detection models constructed by networks such as ResNet, DenseNet and MSDNet.

scholarly journals A Framework to Test Resistency of Detection Algorithms for Stepping-Stone Intrusion on Time-Jittering Manipulation

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lixin Wang ◽  
Jianhua Yang ◽  
Michael Workman ◽  
Peng-Jun Wan
Keyword(s):  
Data Stream ◽  
Detection Method ◽  
Detection Algorithm ◽  
Efficient Algorithms ◽  
Detection Methods ◽  
The Internet ◽  
Stepping Stones ◽  
Stepping Stone ◽  
Detection Algorithms ◽  
A Chain

Hackers on the Internet usually send attacking packets using compromised hosts, called stepping-stones, in order to avoid being detected and caught. With stepping-stone attacks, an intruder remotely logins these stepping-stones using programs like SSH or telnet, uses a chain of Internet hosts as relay machines, and then sends the attacking packets. A great number of detection approaches have been developed for stepping-stone intrusion (SSI) in the literature. Many of these existing detection methods worked effectively only when session manipulation by intruders is not present. When the session is manipulated by attackers, there are few known effective detection methods for SSI. It is important to know whether a detection algorithm for SSI is resistant on session manipulation by attackers. For session manipulation with chaff perturbation, software tools such as Scapy can be used to inject meaningless packets into a data stream. However, to the best of our knowledge, there are no existing effective tools or efficient algorithms to produce time-jittered network traffic that can be used to test whether an SSI detection method is resistant on intruders’ time-jittering manipulation. In this paper, we propose a framework to test resistency of detection algorithms for SSI on time-jittering manipulation. Our proposed framework can be used to test whether an existing or new SSI detection method is resistant on session manipulation by intruders with time-jittering.

scholarly journals Detection of Outliers in a Time Series of Available Parking Spaces

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Yanjie Ji ◽  
Dounan Tang ◽  
Weihong Guo ◽  
Phil T. Blythe ◽  
Gang Ren
Keyword(s):  
Outlier Detection ◽  
Detection Method ◽  
Experimental Tests ◽  
Detection Algorithm ◽  
Phase Detection ◽  
Real World Data ◽  
Two Phase ◽  
Real Time Information ◽  
The City ◽  
Parking Facilities

With the provision of any source of real-time information, the timeliness and accuracy of the data provided are paramount to the effectiveness and success of the system and its acceptance by the users. In order to improve the accuracy and reliability of parking guidance systems (PGSs), the technique of outlier mining has been introduced for detecting and analysing outliers in available parking space (APS) datasets. To distinguish outlier features from the APS’s overall periodic tendency, and to simultaneously identify the two types of outliers which naturally exist in APS datasets with intrinsically distinct statistical features, a two-phase detection method is proposed whereby an improved density-based detection algorithm named “local entropy based weighted outlier detection” (EWOD) is also incorporated. Real-world data from parking facilities in the City of Newcastle upon Tyne was used to test the hypothesis. Thereafter, experimental tests were carried out for a comparative study in which the outlier detection performances of the two-phase detection method, statistic-based method, and traditional density-based method were compared and contrasted. The results showed that the proposed method can identify two different kinds of outliers simultaneously and can give a high identifying accuracy of 100% and 92.7% for the first and second types of outliers, respectively.

scholarly journals Data Streams Oriented Outlier Detection Method: A Fast Minimal Infrequent Pattern Mining

2021 ◽  
Author(s):  
ZhongYu Zhou ◽  
DeChang Pi
Keyword(s):  
Outlier Detection ◽  
Data Streams ◽  
Pattern Mining ◽  
Detection Method ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Mining Method ◽  
Telemetry Data ◽  
Process Data ◽  
Mining Data Streams

Outlier detection is a common method for analyzing data streams. In the existing outlier detection methods, most of methods compute distance of points to solve certain specific outlier detection problems. However, these methods are computationally expensive and cannot process data streams quickly. The outlier detection method based on pattern mining resolves the aforementioned issues, but the existing methods are inefficient and cannot meet requirements of quickly mining data streams. In order to improve the efficiency of the method, a new outlier detection method is proposed in this paper. First, a fast minimal infrequent pattern mining method is proposed to mine the minimal infrequent pattern from data streams. Second, an efficient outlier detection algorithm based on minimal infrequent pattern is proposed for detecting the outliers in the data streams by mining minimal infrequent pattern. The algorithm proposed in this paper is demonstrated by real telemetry data of a satellite in orbit. The experimental results show that the proposed method not only can be applied to satellite outlier detection, but also is superior to the existing methods.

An Accurate Collision Detection Method for Cable Simulation

2013 ◽  
Vol 347-350 ◽  
pp. 3571-3575
Author(s):  
Shi Fu Xie ◽  
Li Yuan Ma ◽  
Peng Yuan Liu
Keyword(s):  
Collision Detection ◽  
Detection Method ◽  
Computational Cost ◽  
Detection Algorithm ◽  
New Method ◽  
Collision Resolution ◽  
Force Method ◽  
Shortest Distance ◽  
Resolution Scheme ◽  
Cable Segment

In this paper, we present a fast and robust collision detection (CD) and resolution scheme for deformable cable using a new method based on the shortest distance of cable segment axis. We employ a bounding sphere hierarchy (BVH) by exploiting the topology of cable for reducing the collision detection query space. After searching the collision through the bounding sphere hierarchy, the collision detection algorithm will find the two segments which are close enough to require an exact collision check. Furthermore, the exact collision state is decided by our proposed method. Penalty force method is applied to the collision resolution. The comparative experiments show that the proposed method performs more accurate than existing algorithms for deformable cable simulation without substantial computational cost.

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