XML document classification based on ELM

Zhao, Xibin; Wang, Guoren; Bi, Xin; Gong, Pimin; Zhao, Yuhai

doi:10.1016/j.neucom.2010.12.038

Cited by 66 publications

(26 citation statements)

References 17 publications

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“…In particular, Table 13 shows the rankings of 10 classifiers on each dataset over training time. According to the rankings, we computed 31. With ten classifiers and four datasets, F F is distributed according to the F distribution with 10 − 1 = 9 and (10 − 1) × (4 − 1) = 27 degrees of freedom.…”

Section: Statistical Significance Of the Resultsmentioning

confidence: 99%

“…In ELM, the hidden-layer node parameter is mathematically calculated instead of being iteratively tuned; thus, it provides good generalization performance at thousands of times faster speed than traditional popular learning algorithms for feedforward neural networks [31].…”

Section: A Brief Introduction To Elmmentioning

confidence: 99%

“…For example, in [32], ELM was applied for plain text classification by using the one-against-one (OAO) and one-against-all (OAA) decomposition scheme. In [31], an ELM-based XML document classification framework was proposed to improve classification accuracy by exploiting two different voting strategies. A protein secondary prediction framework based on ELM was proposed in [33] to provide good performance at extremely high speed.…”

Section: A Brief Introduction To Elmmentioning

confidence: 99%

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Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine

Yin

Zhao

et al. 2016

Applied Sciences

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Multi-instance multi-label learning is a learning framework, where every object is represented by a bag of instances and associated with multiple labels simultaneously. The existing degeneration strategy-based methods often suffer from some common drawbacks:(1) the user-specific parameter for the number of clusters may incur the effective problem; (2) SVM may bring a high computational cost when utilized as the classifier builder. In this paper, we propose an algorithm, namely multi-instance multi-label (MIML)-extreme learning machine (ELM), to address the problems. To our best knowledge, we are the first to utilize ELM in the MIML problem and to conduct the comparison of ELM and SVM on MIML. Extensive experiments have been conducted on real datasets and synthetic datasets. The results show that MIMLELM tends to achieve better generalization performance at a higher learning speed.

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Section: Statistical Significance Of the Resultsmentioning

confidence: 99%

Section: A Brief Introduction To Elmmentioning

confidence: 99%

Section: A Brief Introduction To Elmmentioning

confidence: 99%

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Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine

Yin

Zhao

et al. 2016

Applied Sciences

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“…Because of its advantage, ELM can be applied in many fields and displayed a significant result [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. As a variant of ELM, distributed ELM (i.e.…”

Section: Introductionmentioning

confidence: 99%

Elastic extreme learning machine for big data classification

Xin

Wang

et al. 2015

Neurocomputing

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“…AÖM, düşük egitim zamanı, çok sınıflı egitim kümelerinde yeni örnekler üzerinde yüksek genelleme özelligi ve herhangi bir egitim parametresi içermemesi gibi avantajlarından dolayı, doküman sınıflandırma [2], biyoenformatik [3], görüntü tanıma [4] gibi bir çok farklı alanda kullanılmaktadır.…”

Section: Introductionunclassified

Classification with Extreme Learning Machine and ensemble algorithms over randomly partitioned data

Çatak

2015

2015 23nd Signal Processing and Communications Applications Conference (SIU)

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Abstract-In this age of Big Data, machine learning based data mining methods are extensively used to inspect large scale data sets. Deriving applicable predictive modeling from these type of data sets is a challenging obstacle because of their high complexity. Opportunity with high data availability levels, automated classification of data sets has become a critical and complicated function. In this paper, the power of applying MapReduce based Distributed AdaBoosting of Extreme Learning Machine (ELM) are explored to build reliable predictive bag of classification models. Thus, (i) dataset ensembles are build; (ii) ELM algorithm is used to build weak classification models; and (iii) build a strong classification model from a set of weak classification models. This training model is applied to the publicly available knowledge discovery and data mining datasets.

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XML document classification based on ELM

Cited by 66 publications

References 17 publications

Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine

Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine

Elastic extreme learning machine for big data classification

Classification with Extreme Learning Machine and ensemble algorithms over randomly partitioned data

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