Journal ArticleDOI
Randomized neural networks for multilabel classification
TLDR
In this paper , the authors proposed noniterative randomization-based neural networks for multilabel classification, which is a supervised learning problem in which input instances belong to multiple output labels.About:
This article is published in Applied Soft Computing.The article was published on 2022-01-01. It has received 8 citations till now. The article focuses on the topics: Computer science & Artificial neural network.read more
Citations
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Journal ArticleDOI
Cyanobacteria blue-green algae prediction enhancement using hybrid machine learning–based gamma test variable selection and empirical wavelet transform
Salim Heddam,Zaher Mundher Yaseen,Mayadah W. Falah,Leonardo Goliatt,Mou Leong Tan,Zulfaqar Sa’adi,Iman Ahmadianfar,Mandeep Kaur Saggi,Amandeep Bhatia,Pijush Samui +9 more
TL;DR: Evaluated machine learning models for modelling cyanobacteria blue-green algae at two rivers located in the USA show that good predictive accuracy was obtained using the RFR model and the ANN and RFR were found to be more accurate compared to the ELM and RVFL models, exhibiting high numerical performances.
Journal ArticleDOI
Direct derivation scheme of DT-RNN algorithm for discrete time-variant matrix pseudo-inversion with application to robotic manipulator
TL;DR: In this article , a discrete time-variant RNN (DT-RNN) was proposed to solve the problem of matrix pseudo-inversion, which does not require the theoretical support of a continuous RNN.
Journal ArticleDOI
Ensemble of self-organizing adaptive maps and dynamic multi-objective optimization for organic Rankine cycle (ORC) under transportation and driving environment
TL;DR: In this article , an ensemble approach of self-organizing adaptive maps and dynamic multi-objective optimization for ORC under driving environment from the perspectives of coupling ORC integration system, variable data selection, parameter coupling correlation, adaptive structure design and multiobjective optimisation is proposed.
Journal ArticleDOI
Performance Enhancement of the Unbalanced Text Classification Problem Through a Modified Chi Square-Based Feature Selection Technique: A Mod-Chi based FS technique
TL;DR: In the study, it was found that the proposed model performs better in terms of precision, recall, f-measure, and hamming losses and is able to select the majority of true positive documents despite an unbalanced class distribution for both the datasets.
References
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Journal ArticleDOI
Fuzzy identification of systems and its applications to modeling and control
T. Takagi,Michio Sugeno +1 more
TL;DR: A mathematical tool to build a fuzzy model of a system where fuzzy implications and reasoning are used is presented and two applications of the method to industrial processes are discussed: a water cleaning process and a converter in a steel-making process.
Journal ArticleDOI
ML-KNN: A lazy learning approach to multi-label learning
Min-Ling Zhang,Zhi-Hua Zhou +1 more
TL;DR: Experiments on three different real-world multi-label learning problems, i.e. Yeast gene functional analysis, natural scene classification and automatic web page categorization, show that ML-KNN achieves superior performance to some well-established multi- label learning algorithms.
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A Review On Multi-Label Learning Algorithms
Min-Ling Zhang,Zhi-Hua Zhou +1 more
TL;DR: This paper aims to provide a timely review on this area with emphasis on state-of-the-art multi-label learning algorithms with relevant analyses and discussions.
Journal ArticleDOI
Learning multi-label scene classification
TL;DR: A framework to handle semantic scene classification, where a natural scene may contain multiple objects such that the scene can be described by multiple class labels, is presented and appears to generalize to other classification problems of the same nature.
Journal ArticleDOI
Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture
C. L. Philip Chen,Zhulin Liu +1 more
TL;DR: Compared with existing deep neural networks, experimental results on the Modified National Institute of Standards and Technology database and NYU NORB object recognition dataset benchmark data demonstrate the effectiveness of the proposed Broad Learning System.