scholarly journals A Distributed Algorithm for the Cluster-Based Outlier Detection Using Unsupervised Extreme Learning Machines

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Xite Wang ◽  
Mei Bai ◽  
Derong Shen ◽  
Tiezheng Nie ◽  
Yue Kou ◽  
...  
Keyword(s):  
Outlier Detection ◽  
Distributed Algorithm ◽  
Credit Card ◽  
Extreme Learning Machines ◽  
Slave Node ◽  
Simulation Experiments ◽  
Important Data ◽  
Learning Machines ◽  
Speed Up ◽  
Effectiveness And Efficiency

Outlier detection is an important data mining task, whose target is to find the abnormal or atypical objects from a given dataset. The techniques for detecting outliers have a lot of applications, such as credit card fraud detection and environment monitoring. Our previous work proposed the Cluster-Based (CB) outlier and gave a centralized method using unsupervised extreme learning machines to compute CB outliers. In this paper, we propose a new distributed algorithm for the CB outlier detection (DACB). On the master node, we collect a small number of points from the slave nodes to obtain a threshold. On each slave node, we design a new filtering method that can use the threshold to efficiently speed up the computation. Furthermore, we also propose a ranking method to optimize the order of cluster scanning. At last, the effectiveness and efficiency of the proposed approaches are verified through a plenty of simulation experiments.

scholarly journals Adaptive Cat Swarm Optimization Algorithm and Its Applications in Vehicle Routing Problems

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xiao-Fang Ji ◽  
Jeng-Shyang Pan ◽  
Shu-Chuan Chu ◽  
Pei Hu ◽  
Qing-Wei Chai ◽  
...  
Keyword(s):  
Vehicle Routing ◽  
Hybrid Algorithm ◽  
Simulation Experiments ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Cat Swarm Optimization ◽  
Fixed Dimension ◽  
Speed Up ◽  
Effectiveness And Efficiency ◽  
Experimental Findings

This paper proposes a novel hybrid algorithm named Adaptive Cat Swarm Optimization (ACSO). It combines the benefits of two swarm intelligence algorithms, CSO and APSO, and presents better search results. Firstly, some strategies are implemented to improve the performance of the proposed hybrid algorithm. The tracing radius of the cat group is limited, and the random number parameter r is adaptive adjusted. In addition, a scaling factor update method, called a memory factor y, is introduced into the proposed algorithm. They can be learnt very well so as to jump out of local optimums and speed up the global convergence. Secondly, by comparing the proposed algorithm with PSO, APSO, and CSO, 23 benchmark functions are verified by simulation experiments, which consists of unimodal, multimodal, and fixed-dimension multimodal. The results show the effectiveness and efficiency of the innovative hybrid algorithm. Lastly, the proposed ACSO is utilized to solve the Vehicle Routing Problem (VRP). Experimental findings also reveal the practicability of the ACSO through a comparison with certain existing methods.

Optimizing Weighted Extreme Learning Machines for imbalanced classification and application to credit card fraud detection

2020 ◽  
Vol 407 ◽  
pp. 50-62 ◽  
Author(s):  
Honghao Zhu ◽  
Guanjun Liu ◽  
Mengchu Zhou ◽  
Yu Xie ◽  
Abdullah Abusorrah ◽  
...  
Keyword(s):  
Credit Card ◽  
Fraud Detection ◽  
Extreme Learning Machines ◽  
Credit Card Fraud ◽  
Imbalanced Classification ◽  
Learning Machines

Extreme learning machines’ ensemble selection with GRASP

2015 ◽  
Vol 43 (2) ◽  
pp. 439-459 ◽  
Author(s):  
Ting Zhang ◽  
Qun Dai ◽  
Zhongchen Ma
Keyword(s):  
Extreme Learning Machines ◽  
Learning Machines ◽  
Ensemble Selection
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