Selected Statistical Methods in QSAR

Roy, Kunal; Kar, Supratik; Das, Rudra Narayan

doi:10.1016/b978-0-12-801505-6.00006-5

Cited by 33 publications

(11 citation statements)

References 20 publications

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“…SVM performs well in higher dimensions (i.e., the number of input features is relatively high, and it has low risk of overfitting). However, it is slow for larger datasets and does not perform well when the data are especially noisy ( Roy, Kar, & Das, 2015 ; Uddin, Khan, Hossain, & Moni, 2019 ; Van Messem, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Evaluation of classification approaches for distinguishing brain states predictive of episodic memory performance from electroencephalography

et al. 2022

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Previous studies have attempted to separate single trial neural responses for events a person is likely to remember from those they are likely to forget using machine learning classification methods. Successful single tria classification holds potential for translation into the clinical realm for real-time detection of memory and other cognitive states to provide real-time interventions (i.e., brain-computer interfaces). However, most of these studies—and classification analyses in general—do not make clear if the chosen methodology is optimally suited for the classification of memory-related brain states. To address this problem, we systematically compared different methods for every step of classification (i.e., feature extraction, feature selection, classifier selection) to investigate which methods work best for decoding episodic memory brain states—the first analysis of its kind. Using an adult lifespan sample EEG dataset collected during performance of an episodic context encoding and retrieva task, we found that no specific feature type (including Common Spatial Pattern (CSP)-based features, mean, variance, correlation, features based on AR model, entropy, phase, and phase synchronization) outperformed others consistently in distinguishing different memory classes. However, extracting all of these feature types consistently outperformed extracting only one type of feature. Additionally, the combination of filtering and sequential forward selection was the optimal method to select the effective features compared to filtering alone or performing no feature selection at all. Moreover, although all classifiers performed at a fairly similar level, LASSO was con sistently the highest performing classifier compared to other commonly used options (i.e., naïve Bayes, SVM, and logistic regression) while naïve Bayes was the fastest classifier. Lastly, for multiclass classification (i.e., levels of context memory confidence and context feature perception), generalizing the binary classification using the binary decision tree performed better than the voting or one versus rest method. These methods were shown to outperform alternative approaches for three orthogonal datasets (i.e., EEG working memory, EEG motor imagery, and MEG working memory), supporting their generalizability. Our results provide an optimized methodological process for classifying single-trial neural data and provide important insight and recommendations for a cognitive neuroscientist’s ability to make informed choices at all stages of the classification process for predicting memory and other cognitive states.

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Section: Methodsmentioning

confidence: 99%

Evaluation of classification approaches for distinguishing brain states predictive of episodic memory performance from electroencephalography

et al. 2022

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“…A dendrogram was generated to show the hierarchal relationship between the clusters. Euclidean distances were computed from raw data (Roy et al., 2015); the Euclidean distance between two n‐dimensional vectors x and y was calculated as:

d_{x, y} = \sqrt{\sum_{i = 1}^{n} {(x_{i} ‐ y_{i})}^{2}}

…”

Section: Methodsmentioning

confidence: 99%

Feasibility of a single‐species quota system for management of the Malaysian multispecies purse‐seine fishery

Harlyan

Matsuishi

Saleh³

2021

Fisheries Management Eco

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Malaysian fisheries employ multiple measures to improve management; however, not all are well‐suited to the multispecies fisheries. As part of a pilot project, an individual quota system was introduced for the purse‐seine fishery off the East Coast of Peninsular Malaysia (ECPM), but no assessment of this particular measure nor the feasibly of its implementation has been confirmed. Therefore, this study analysed spatial and temporal patterns of purse‐seine fishing, by collecting catch composition data per landing and its fishing ground within three period fishery surveys between August 2017 and September 2018 at six different landing sites. Similarity and cluster analysis examined species composition and diversity to determine the feasibility of implementing a single‐species quota system in this multispecies fishery. Some overlapped of indices results and minor difference in catch composition were found due to changes in spatial and temporal fishing activities. However, no specific spatial or temporal patterns were discernible as structuring the fishing grounds used by purse‐seiners. The absence of patterns, using the available data, might be attributable to huge species aggregations and widely distributed and homogenously mixed fish stocks. Thus, it is likely impractical to manage species individually in such a multispecies fishery.

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“…The inner relation is improved by exchanging the scores, Y P and X P , in an iterative calculation. This allows information from one block to be used to adjust the orientation of the latent vectors in the other block, and vice versa (Roy, Kar, & Das, 2015; Zhu et al., 2006).…”

Section: Methodsmentioning

confidence: 99%

Chemometric strategies for nondestructive and rapid assessment of nitrate content in harvested spinach using Vis‐NIR spectroscopy

Mahanti

Chakraborty

Kotwaliwale

et al. 2020

Journal of Food Science

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The overuse of nitrogenous fertilizers leads to an increase in the nitrate content of green leafy vegetables. Consumption of food with excess nitrate is not advisable because it results in human ailment. In this study, spinach leaves were harvested from plants grown under nine varying (0 to 400 kg/ha) nitrogenous fertilizer doses. A total of 261 samples were used to predict the nitrate content in spinach leaves using Vis-NIR (350 to 2,500 nm). The nitrate content was measured destructively using the ion-selective conductive method. Partial least square (PLS) regression models were developed using whole spectra and featured wavelengths. Spectral data were pre-processed using different spectral pre-processing techniques such as Savitzky-Golay (SG) derivative, standard normal variate (SNV), multiplicative scatter correction (MSC), baseline correction, and detrending. The predictive accuracy of the PLS model had improved after preprocessing of spectral data with MSC (RPD CV = 1.767; SE CV = 545.745; bias CV = −3.107; slope CV = 0.698) and SNV (RPD CV = 1.768; SE CV = 545.337; bias CV = −3.201; slope CV = 0.698) technique, but this was not significant (P < 0.05) as compared with raw spectral data (RPD CV = 1.679; SE CV = 572.669; bias CV = −7.046; slope CV = 0.687). The effective wavelengths for measurement nitrate content in spinach leaves were identified as 558, 706, 780, 1,000, and 1,420 nm. The performance of PLS model developed with effective wavelengths also had good prediction accuracy (RPD CV = 1.482; SE CV = 648.672; bias CV = −3.805; slope CV = 0.565) but significantly lower than the performance of model developed with full spectral data. The overall results of this study suggest that Vis-NIR spectroscopy can be an important tool and has great potential for the rapid and nondestructive assessment of nitrate content in harvested spinach, with a view to ascertain the suitability of the harvest for food uses.

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Selected Statistical Methods in QSAR

Cited by 33 publications

References 20 publications

Evaluation of classification approaches for distinguishing brain states predictive of episodic memory performance from electroencephalography

Evaluation of classification approaches for distinguishing brain states predictive of episodic memory performance from electroencephalography

Feasibility of a single‐species quota system for management of the Malaysian multispecies purse‐seine fishery

Chemometric strategies for nondestructive and rapid assessment of nitrate content in harvested spinach using Vis‐NIR spectroscopy

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