Proceedings of ELM-2015 Volume 1: Theory, Algorithms and Applications (I)

516 Pages·2016·14.789 MB·English

by  Jiuwen Cao, Kezhi Mao, Jonathan Wu, Amaury Lendasse (eds.)

Proceedings in Adaptation, Learning and Optimization 6
Jiuwen Cao
Kezhi Mao
Jonathan Wu
Amaury Lendasse Editors
Proceedings
of ELM-2015
Volume 1
Theory, Algorithms and Applications (I)
Proceedings in Adaptation, Learning
and Optimization
Volume 6
Series editors
Yew Soon Ong, Nanyang Technological University, Singapore
e-mail: [e-mail]
Meng-Hiot Lim, Nanyang Technological University, Singapore
e-mail: [e-mail]
Board of editors
Hussain Abbas, University of New South Wales, Australia
Giovanni Acampora, Nottingham Trent University, Nottingham, UK
Enrique Alba, University of Málaga, Málaga, Spain
Jonathan Chan, King Mongkut’s University of Technology Thonburi (KMUTT),
Bangkok, Thailand
Sung-Bae Cho, Yonsei University, Seoul, Korea
Hisao Ishibuchi, Osaka Prefecture University, Osaka, Japan
Wilfried Jakob, Institute for Applied Computer Science (IAI), Germany
Jose A. Lozano, University of the Basque Country UPV/EHU, Spain
Zhang Mengjie, Victoria University of Wellington, Wellington, New Zealand
Jim Smith, University of the West of England, Bristol, UK
Kay-Chen Tan, National University of Singapore, Singapore
Ke Tang, School of Computer Science and Technology, China
Chuang-Kang Ting, National Chung Cheng University, Taiwan
Donald C. Wunsch, Missouri University of Science & Technology, USA
Jin Yaochu, University of Surrey, UK
About this Series
The role of adaptation, learning and optimization are becoming increasingly
essential and intertwined. The capability of a system to adapt either through
modification of its physiological structure or via some revalidation process of
internalmechanismsthatdirectlydictatetheresponseorbehavioriscrucialinmany
real world applications. Optimization lies at the heart of most machine learning
approaches while learning and optimization are two primary means to effect
adaptation in various forms. They usually involve computational processes
incorporated within the system that trigger parametric updating and knowledge
or model enhancement, giving rise to progressive improvement. This book series
serves as a channel to consolidate work related to topics linked to adaptation,
learning and optimization in systems and structures. Topics covered under this
series include:
(cid:129) complex adaptive systems including evolutionary computation, memetic com-
puting, swarm intelligence, neural networks, fuzzy systems, tabu search, simu-
lated annealing, etc.
(cid:129) machine learning, data mining & mathematical programming
(cid:129) hybridization of techniques that span across artificial intelligence and compu-
tational intelligence for synergistic alliance of strategies for problem-solving
(cid:129) aspects of adaptation in robotics
(cid:129) agent-based computing
(cid:129) autonomic/pervasive computing
(cid:129) dynamic optimization/learning in noisy and uncertain environment
(cid:129) systemic alliance of stochastic and conventional search techniques
(cid:129) all aspects of adaptations in man-machine systems.
This book series bridges the dichotomy of modern and conventional mathematical
and heuristic/meta-heuristics approaches to bring about effective adaptation,
learning and optimization. It propels the maxim that the old and the new can
come together and be combined synergistically to scale new heights in
problem-solving. To reach such a level, numerous research issues will emerge
and researchers will find the book series a convenient medium to track the
progresses made.
More information about this series at http://www.springer.com/series/13543
⋅
Jiuwen Cao Kezhi Mao
⋅
Jonathan Wu Amaury Lendasse
Editors
Proceedings of ELM-2015
Volume 1
Theory, Algorithms and Applications (I)
123
Editors
JiuwenCao Jonathan Wu
Institute of Information andControl Department ofElectrical andComputer
HangzhouDianziUniversity Engineering
Hangzhou, Zhejiang University of Windsor
China Windsor, ON
Canada
Kezhi Mao
Schoolof Electrical andElectronic Amaury Lendasse
Engineering Department ofMechanical andIndustrial
NanyangTechnological University Engineering
Singapore University of Iowa
Singapore Iowa City,IA
USA
ISSN 2363-6084 ISSN 2363-6092 (electronic)
Proceedings inAdaptation, Learning andOptimization
ISBN978-3-319-28396-8 ISBN978-3-319-28397-5 (eBook)
DOI 10.1007/978-3-319-28397-5
LibraryofCongressControlNumber:2015958845
©SpringerInternationalPublishingSwitzerland2016
Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpart
of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,
recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission
orinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilar
methodologynowknownorhereafterdeveloped.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this
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The publisher, the authors and the editors are safe to assume that the advice and information in this
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authorsortheeditorsgiveawarranty,expressorimplied,withrespecttothematerialcontainedhereinor
foranyerrorsoromissionsthatmayhavebeenmade.
Printedonacid-freepaper
ThisSpringerimprintispublishedbySpringerNature
TheregisteredcompanyisSpringerInternationalPublishingAGSwitzerland
Contents
Extreme Learning Machine for Multi-class Sentiment Classification
of Tweets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Zhaoxia Wang and Yogesh Parth
Efficient Batch Parallel Online Sequential Extreme Learning
Machine Algorithm Based on MapReduce . . . . . . . . . . . . . . . . . . . . . 13
Shan Huang, Botao Wang, Yuemei Chen, Guoren Wang and Ge Yu
Fixed-Point Evaluation of Extreme Learning Machine
for Classification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Yingnan Xu, Jingfei Jiang, Juping Jiang, Zhiqiang Liu and Jinwei Xu
Multi-layer Online Sequential Extreme Learning Machine
for Image Classification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Bilal Mirza, Stanley Kok and Fei Dong
ELM Meets Urban Computing: Ensemble Urban Data
for Smart City Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
Ningyu Zhang, Huajun Chen, Xi Chen and Jiaoyan Chen
Local and Global Unsupervised Kernel Extreme Learning Machine
and Its Application in Nonlinear Process Fault Detection . . . . . . . . . . 65
Hanyuan Zhang, Xuemin Tian, Xiaohui Wang and Yuping Cao
Parallel Multi-graph Classification Using Extreme Learning
Machine and MapReduce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
Jun Pang, Yu Gu, Jia Xu, Xiaowang Kong and Ge Yu
Extreme Learning Machine for Large-Scale Graph Classification
Based on MapReduce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
Zhanghui Wang, Yuhai Zhao and Guoren Wang
v
vi Contents
The Distance-Based Representative Skyline Calculation
Using Unsupervised Extreme Learning Machines . . . . . . . . . . . . . . . . 107
Mei Bai, Junchang Xin, Guoren Wang and Xite Wang
Multi-label Text Categorization Using L -norm Minimization
21
Extreme Learning Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
Mingchu Jiang, Na Li and Zhisong Pan
Cluster-Based Outlier Detection Using Unsupervised Extreme
Learning Machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
Xite Wang, Derong Shen, Mei Bai, Tiezheng Nie,
Yue Kou and Ge Yu
Segmentation of the Left Ventricle in Cardiac MRI
Using an ELM Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
Yang Luo, Benqiang Yang, Lisheng Xu, Liling Hao, Jun Liu,
Yang Yao and Frans van de Vosse
Channel Estimation Based on Extreme Learning Machine
for High Speed Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
Fang Dong, Junbiao Liu, Liang He, Xiaohui Hu and Hong Liu
MIMO Modeling Based on Extreme Learning Machine. . . . . . . . . . . . 169
Junbiao Liu, Fang Dong, Jiuwen Cao and Xinyu Jin
Graph Classification Based on Sparse Graph Feature Selection
and Extreme Learning Machine. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
Yajun Yu, Zhisong Pan and Guyu Hu
Time Series Prediction Based on Online Sequential Improved
Error Minimized Extreme Learning Machine. . . . . . . . . . . . . . . . . . . 193
Jiao Xue, Zeshen Liu, Yong Gong and Zhisong Pan
Adaptive Input Shaping for Flexible Systems Using an Extreme
Learning Machine Algorithm Identification . . . . . . . . . . . . . . . . . . . . 211
Jun Hu and Zhongyi Chu
Kernel Based Semi-supervised Extreme Learning Machine
and the Application in Traffic Congestion Evaluation. . . . . . . . . . . . . 227
Qing Shen, Xiaojuan Ban, Chong Guo and Cong Wang
Improvement of ELM Algorithm for Multi-object Identification
in Gesture Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237
Liang Diao, Liguo Shuai, Huiling Chen and Weihang Zhu
Contents vii
SVM and ELM: Who Wins? Object Recognition with Deep
Convolutional Features from ImageNet. . . . . . . . . . . . . . . . . . . . . . . . 249
Lei Zhang, David Zhang and Fengchun Tian
Learning with Similarity Functions: A Novel Design
for the Extreme Learning Machine. . . . . . . . . . . . . . . . . . . . . . . . . . . 265
Federica Bisio, Paolo Gastaldo, Rodolfo Zunino,
Christian Gianoglio and Edoardo Ragusa
A Semi-supervised Low Rank Kernel Learning Algorithm
via Extreme Learning Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279
Bing Liu, Mingming Liu, Chen Zhang and Weidong Wang
Application of Extreme Learning Machine on Large Scale
Traffic Congestion Prediction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293
Xiaojuan Ban, Chong Guo and Guohui Li
Extreme Learning Machine-Guided Collaborative Coding
for Remote Sensing Image Classification. . . . . . . . . . . . . . . . . . . . . . . 307
Chunwei Yang, Huaping Liu, Shouyi Liao and Shicheng Wang
Distributed Weighted Extreme Learning Machine for Big
Imbalanced Data Learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319
Zhiqiong Wang, Junchang Xin, Shuo Tian and Ge Yu
NMR Image Segmentation Based on Unsupervised Extreme
Learning Machine. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333
Junchang Xin, Zhongyang Wang, Shuo Tian and Zhiqiong Wang
Annotating Location Semantic Tags in LBSN Using Extreme
Learning Machine. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347
Xiangguo Zhao, Zhen Zhang, Xin Bi, Xin Yu and Jingtao Long
Feature Extraction of Motor Imagery EEG Based on Extreme
Learning Machine Auto-encoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361
Lijuan Duan, Yanhui Xu, Song Cui, Juncheng Chen
and Menghu Bao
Multimodal Fusion Using Kernel-Based ELM for Video
Emotion Recognition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371
Lijuan Duan, Hui Ge, Zhen Yang and Juncheng Chen
viii Contents
Equality Constrained-Optimization-Based Semi-supervised ELM
for Modeling Signal Strength Temporal Variation in Indoor
Location Estimation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383
Felis Dwiyasa, Meng-Hiot Lim, Yew-Soon Ong and Bijaya Panigrahi
Extreme Learning Machine with Gaussian Kernel Based
Relevance Feedback Scheme for Image Retrieval . . . . . . . . . . . . . . . . 397
Lijuan Duan, Shuai Dong, Song Cui and Wei Ma
Routing Tree Maintenance Based on Trajectory Prediction
in Mobile Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409
Junchang Xin, Teng Li, Pei Wang and Zhiqiong Wang
Two-Stage Hybrid Extreme Learning Machine
for Sequential Imbalanced Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423
Wentao Mao, Jinwan Wang, Ling He and Yangyang Tian
Feature Selection and Modelling of a Steam Turbine
from a Combined Heat and Power Plant Using ELM . . . . . . . . . . . . . 435
Sandra Seijo, Victoria Martínez, Inés del Campo,
Javier Echanobe and Javier García-Sedano
On the Construction of Extreme Learning Machine
for One Class Classifier. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447
Chandan Gautam and Aruna Tiwari
Record Linkage for Event Identification in XML Feeds Stream
Using ELM. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 463
Xin Bi, Xiangguo Zhao, Wenhui Ma, Zhen Zhang and Heng Zhan
Timeliness Online Regularized Extreme Learning Machine. . . . . . . . . 477
Xiong Luo, Xiaona Yang, Changwei Jiang and Xiaojuan Ban
An Efficient High-Dimensional Big Data Storage Structure
Based on US-ELM. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489
Linlin Ding, Yu Liu, Baoyan Song and Junchang Xin
An Enhanced Extreme Learning Machine for Efficient
Small Sample Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501
Ying Yin, Yuhai Zhao, Ming Li and Bin Zhang
Contents ix
Code Generation Technology of Digital Satellite . . . . . . . . . . . . . . . . . 511
Ren Min, Dong Yunfeng and Li Chang
Class-Constrained Extreme Learning Machine . . . . . . . . . . . . . . . . . . 521
Xiao Liu, Jun Miao, Laiyun Qing and Baoxiang Cao
Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 531