Robust support vector machine-trained fuzzy system

Forghani, Yahya; Yazdi, Hadi Sadoghi

doi:10.1016/j.neunet.2013.11.013

Cited by 19 publications

(9 citation statements)

References 15 publications

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“…Consequently, new samples are mapped into the created spaces and simultaneously allocated to their respective categories, based on their relative positions from the gaps. While SVM outperforms other methods on the basis of linearly separable problems, it has also been criticized for the difficulties associated with data training and interpretation [43], [44].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Towards developing a systematic knowledge trend for building energy consumption prediction

Qiao

Yunusa‐Kaltungo

Edwards

2021

Journal of Building Engineering

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Towards developing a systematic knowledge trend for building energy consumption prediction

Qiao

Yunusa‐Kaltungo

Edwards

2021

Journal of Building Engineering

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“…Several classification algorithms, including support vector model (SVM) [43], artificial neural networks (ANN) [44] and random forest (RF) [45], have been used for bamboo mapping successfully. However, the SVM consumes a lot of machine memory and computing time when handling multi-dimensional features for classification, and it is difficult to select certain parameters, such as the kernel function [46,47]. While ANNs are still in the development stage and is hard to use and to optimize because of the time-consuming parameter tuning procedure, there are numerous types of neural network architectures to choose from and a high number of algorithms used for training [48].…”

Section: Introductionmentioning

confidence: 99%

Bamboo Forest Mapping in China Using the Dense Landsat 8 Image Archive and Google Earth Engine

Song

Liu

et al. 2022

Remote Sensing

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It is of great significance to understand the extent and distribution of bamboo for its valuable ecological services and economic benefits. However, it is challenging to map bamboo using remote sensing images over a large area because of the similarity between bamboo and other vegetation types, the availability of clear optical images, huge workload of image processing, and sample collection. In this study, we use the Landsat 8 times series images archive to map bamboo forests in China via the Google Earth engine. Several spectral indices were calculated and used as classification features, including the normalized difference vegetation index (NDVI), the normalized difference moisture index (NDMI) and textural features of the gray-level co-occurrence matrix (GLCM). We found that the bamboo forest covered an area of 709.92 × 104 hectares, with the provinces of Fujian, Jiangxi, and Zhejiang containing the largest area concentrations. The bamboo forest map was accurate and reliable with an average producer’s accuracy of 89.97%, user’s accuracy of 78.45% and kappa coefficient of 0.7789. In addition, bamboo was mainly distributed in forests with an elevation of 300–1200 m above sea level, average annual precipitation of 1200–1500 mm and average day land surface temperature of 19–25 °C. The NDMI is particularly useful in differentiating bamboo from other vegetation because of the clear difference in canopy moisture content, whilst NDVI and elevation are also helpful to improve the bamboo classification accuracy. The bamboo forest map will be helpful for bamboo forest industry planning and could be used for evaluating the ecological service of the bamboo forest.

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“…A key 72 ability of machine learning tools, in its most fundamental form, is their ability to 73 generalize complex patterns and making intelligent decisions from data. (Sing et al, 74 2016;Forghani and Yazdi, 2014;Ma et al, 2012). Machine learning will also 75 enhance our understanding of pathogen-plant interactions as well as the interaction 76 of plants with other stresses (Kuska et al 2015;Sing et al 2016;Romer et al, 77 2012).…”

Section: Introduction 30mentioning

confidence: 99%

Molecular Predicting Drought Tolerance in Maize Inbred Lines by Machine Learning Approaches

Beiki

2019

Preprint

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Learning Approaches 2Drought is one of the prime abiotic stresses in the world. Now, amongst the new 3 technologies available for speed up the releasing of new drought tolerance 4 genotypes, there is an emanate discipline called machine learning. The study 5 presents Machine Learning for identification, classification and prediction of drought 6 tolerance maize inbred lines based on SSR genetic markers datasets generated from 7 PCR reactions. A total of 356 SSR reproducible fragment alleles were detected 8 across the 71 polymorphic SSR loci. A dataset of 12 inbred lines with these fragments 9 prepared as attributes and was imported into RapidMiner software. After removal of 10 duplicates, useless and correlated features, 311 feature attributes were polymorphic, 11 ranging in size from 1500 to 3500 bp. The most important attribute fragment alleles in 12 different attribute weighting selected. Ten datasets created using attribute selection 13 (weighting) algorithms. Different classification algorithms were applied on datasets. 14 These can be used to identify groups of alleles with similar patterns of expression, 15and are able to create some models that have been applied successfully in the 16 prediction, classification and pattern recognition in drought stress. Some 17 unsupervised models were able to differentiate tolerant inbred lines from susceptible. 18Four unsupervised models were able to produce the different decision trees with root 19 and leaves. The most important attribute alleles almost in all of models were 20 phi033a3, bnlg1347a1 and bnlg172a2 respectively, that can help to identify tolerant 21 maize inbred lines with high precision. 22 23

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Robust support vector machine-trained fuzzy system

Cited by 19 publications

References 15 publications

Towards developing a systematic knowledge trend for building energy consumption prediction

Towards developing a systematic knowledge trend for building energy consumption prediction

Bamboo Forest Mapping in China Using the Dense Landsat 8 Image Archive and Google Earth Engine

Molecular Predicting Drought Tolerance in Maize Inbred Lines by Machine Learning Approaches

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