Weighted extreme learning machine for imbalance learning

doi:10.1016/J.NEUCOM.2012.08.010

Home
/
Papers
/
Weighted extreme learning machine for imbalance learning

Journal Article•DOI•

Weighted extreme learning machine for imbalance learning

Weiwei Zong¹, Guang-Bin Huang¹, Yiqiang Chen²•Institutions (2)

Nanyang Technological University¹, Chinese Academy of Sciences²

01 Feb 2013-Neurocomputing (Elsevier Science Publishers B. V.)-Vol. 101, pp 229-242

TL;DR: A weighted ELM which is able to deal with data with imbalanced class distribution while maintain the good performance on well balanced data as unweighted ELM and generalized to cost sensitive learning.

read less

About: This article is published in Neurocomputing.The article was published on 2013-02-01. It has received 627 citations till now. The article focuses on the topics: Extreme learning machine & Active learning (machine learning).

...read moreread less

Citations

PDF

Open Access

More filters

Journal Article•DOI•

An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics

[...]

Victoria López¹, Alberto Fernández², Salvador García², Vasile Palade³, Francisco Herrera¹ - Show less +1 more•Institutions (3)

University of Granada¹, University of Jaén², University of Oxford³

20 Nov 2013-Information Sciences

TL;DR: This work carries out a thorough discussion on the main issues related to using data intrinsic characteristics in this classification problem, and introduces several approaches and recommendations to address these problems in conjunction with imbalanced data.

...read moreread less

1,292 citations

Cites background from "Weighted extreme learning machine f..."

...These approaches can be categorized into two groups: the internal approaches that create new algorithms or modify existing ones to take the class-imbalance problem into consideration [7,41,82,129,152] and external approaches that preprocess the data in order to diminish the effect of their class imbalance [9,43]....
[...]

Journal Article•DOI•

Trends in extreme learning machines

[...]

Gao Huang¹, Guang-Bin Huang², Shiji Song¹, Keyou You¹•Institutions (2)

Tsinghua University¹, Nanyang Technological University²

01 Jan 2015-Neural Networks

TL;DR: In this paper, the authors report the current state of the theoretical research and practical advances on this subject and provide a comprehensive view of these advances in ELM together with its future perspectives.

...read moreread less

1,289 citations

Journal Article•DOI•

Semi-Supervised and Unsupervised Extreme Learning Machines

[...]

Gao Huang¹, Shiji Song¹, Jatinder N. D. Gupta², Cheng Wu¹•Institutions (2)

Tsinghua University¹, University of Alabama in Huntsville²

12 Mar 2014-IEEE Transactions on Systems, Man, and Cybernetics

TL;DR: It is shown in this paper that all the supervised, semi-supervised, and unsupervised ELMs can actually be put into a unified framework, which provides new perspectives for understanding the mechanism of random feature mapping, which is the key concept in ELM theory.

...read moreread less

Abstract: Extreme learning machines (ELMs) have proven to be efficient and effective learning mechanisms for pattern classification and regression. However, ELMs are primarily applied to supervised learning problems. Only a few existing research papers have used ELMs to explore unlabeled data. In this paper, we extend ELMs for both semi-supervised and unsupervised tasks based on the manifold regularization, thus greatly expanding the applicability of ELMs. The key advantages of the proposed algorithms are as follows: 1) both the semi-supervised ELM (SS-ELM) and the unsupervised ELM (US-ELM) exhibit learning capability and computational efficiency of ELMs; 2) both algorithms naturally handle multiclass classification or multicluster clustering; and 3) both algorithms are inductive and can handle unseen data at test time directly. Moreover, it is shown in this paper that all the supervised, semi-supervised, and unsupervised ELMs can actually be put into a unified framework. This provides new perspectives for understanding the mechanism of random feature mapping, which is the key concept in ELM theory. Empirical study on a wide range of data sets demonstrates that the proposed algorithms are competitive with the state-of-the-art semi-supervised or unsupervised learning algorithms in terms of accuracy and efficiency.

...read moreread less

678 citations

Cites background or methods from "Weighted extreme learning machine f..."

...for specific problems, such as ELMs for online sequential data [29]–[31], ELMs for noisy/missing data [32]–[34], ELMs for imbalanced data [35], and so on....
[...]
...Note that similar to the weighted ELM algorithm (W-ELM) introduced in [35], here, we associate different penalty coefficient Ci on the prediction errors with respect to patterns from different classes....
[...]

Journal Article•DOI•

The impact of class imbalance in classification performance metrics based on the binary confusion matrix

[...]

Amalia Luque¹, Alejandro Carrasco¹, Alejandro Martín¹, Ana de las Heras¹•Institutions (1)

University of Seville¹

01 Jul 2019-Pattern Recognition

TL;DR: A new way to measure the imbalance is defined which surpasses the Imbalance Ratio and a set of null-biased multi-perspective Class Balance Metrics is proposed which extends the concept of Class Balance Accuracy to other performance metrics.

...read moreread less

540 citations

Journal Article•DOI•

Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment

[...]

Mohammad Jamshidi¹, Ali Lalbakhsh², Jakub Talla¹, Zdenek Peroutka¹, Farimah Hadjilooei³, Pedram Lalbakhsh⁴, Morteza Jamshidi⁵, Luigi La Spada⁶, Mirhamed Mirmozafari⁷, Mojgan Dehghani⁸, Asal Sabet⁹, Saeed Roshani⁵, Sobhan Roshani⁵, Nima Bayat-Makou¹⁰, Bahare Mohamadzade², Zahra Malek⁵, Alireza Jamshidi¹¹, Sarah Kiani¹¹, Hamed Hashemi-Dezaki¹, Wahab Mohyuddin¹ - Show less +16 more•Institutions (11)

University of West Bohemia¹, Macquarie University², Tehran University of Medical Sciences³, Razi University⁴, Islamic Azad University⁵, Edinburgh Napier University⁶, University of Wisconsin-Madison⁷, Louisiana State University⁸, Texas A&M University–Kingsville⁹, University of Toronto¹⁰, Babol University of Medical Sciences¹¹

12 Jun 2020-IEEE Access

TL;DR: A response to combat the virus through Artificial Intelligence (AI) is rendered in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers.

...read moreread less

Abstract: COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19's spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Artificial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long/Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Artificial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications.

...read moreread less

358 citations

Cites background from "Weighted extreme learning machine f..."

...There are several other studies that have previously scrutinized ELM with fixed network architectures [30], [32]–[34]....
[...]

1
2
3
4
…
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126

Collapse

References

PDF

Open Access

More filters

Statistical learning theory

[...]

Vladimir Vapnik

01 Jan 1998

TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.

...read moreread less

Abstract: A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.

...read moreread less

26,531 citations

"Weighted extreme learning machine f..." refers background in this paper

...From optimization point of view, connections between ELM and the popular support vector machines (SVMs) exist mainly in the aspects of problem formulation, network architecture except that solutions of SVMs are suboptimal compared to those of ELM [5,6]....
[...]
...Note that another popular machine learning technique support vector machine (SVM) [20,21], originally a binary classifier, fails to be applied to the multiclass problems directly without modification....
[...]
...Similar to SVMs which aim to minimize the training errors and maximize the marginal distance between two classes, the goal of ELM is the same: Minimize : JHb TJ2 and JbJ ð4Þ Similar to LS-SVM, the optimization problem is mathematically written as Minimize : LPELM ¼ 1 2 JbJ2þC 1 2 XN i ¼ 1 JniJ 2 Subject to : hðxiÞb¼ tTi n T i , i¼ 1, . . . ,N ð5Þ where ni ¼ ½xi,1, . . . ,xi,m T is the training error vector of the m output nodes with respect to the training sample xi....
[...]
...Usually the multiclass problem is decomposed into binary subproblems, implemented by multiple SVMs....
[...]
...To find out the predicted label of x, users just need to refer to a simple equation as below: labelðxÞ ¼ arg max f iðxÞ, iAf1, . . . ,mg ð16Þ Note that another popular machine learning technique support vector machine (SVM) [20,21], originally a binary classifier, fails to be applied to the multiclass problems directly without modification....
[...]

UCI Machine Learning Repository

[...]

A. Asuncion

01 Jan 2007

17,341 citations

Journal Article•DOI•

An introduction to ROC analysis

[...]

Tom Fawcett

01 Jun 2006-Pattern Recognition Letters

TL;DR: The purpose of this article is to serve as an introduction to ROC graphs and as a guide for using them in research.

...read moreread less

17,017 citations

"Weighted extreme learning machine f..." refers methods in this paper

...Another interesting tool, receiver operating characteristics (ROC) graph [16], provides a visual illustration of the performance of classifiers on binary datasets, where a classifier corresponds to a point....
[...]

Journal Article•DOI•

Extreme learning machine: Theory and applications

[...]

Guang-Bin Huang, Qin-Yu Zhu, Chee Kheong Siew

01 Dec 2006-Neurocomputing

TL;DR: A new learning algorithm called ELM is proposed for feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs which tends to provide good generalization performance at extremely fast learning speed.

...read moreread less

10,217 citations

"Weighted extreme learning machine f..." refers background in this paper

...Extreme learning machine (ELM) [1,2,11] was originally proposed for the single-hidden layer feedforward neural networks and was then extended to the ‘‘generalized’’ single-hidden layer feedforward networks (SLFNs) where the hidden layer need not be neuron alike [3,4]....
[...]

Journal Article•DOI•

Practical Methods of Optimization.

[...]

Christoph Witzgall, Roger Fletcher

01 Oct 1989-Mathematics of Computation

9,153 citations