A Supervised Filter Feature Selection method for mixed data based on Spectral Feature Selection and Information-theory redundancy analysis

Solorio-Fernández, Saúl; Martínez-Trinidad, José Fco.; Carrasco-Ochoa, Jesús Ariel

doi:10.1016/j.patrec.2020.07.039

Cited by 31 publications

(13 citation statements)

References 23 publications

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“…Information theory could be applied to select features with low redundancy to maximize the information content contained within a chemical fingerprint. 47 Nevertheless, we recognize that some chemical clustering is expected, given that features are from the same source, and we also recognize that in some situations, having diagnostic features with similar fate and transport characteristics could be advantageous (e.g., nonsorptive features that transport well on the subsurface). Therefore, it is important to have a strong understanding of both the needs of a diagnostic fingerprint and the limitations of the chemical processing method used to develop the fingerprint.…”

Section: ■ Results and Discussionmentioning

confidence: 92%

Machine Learning Applications for Chemical Fingerprinting and Environmental Source Tracking Using Non-target Chemical Data

Dávila-Santiago

Shi

Mahadwar

et al. 2022

Environ. Sci. Technol.

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A frequent goal of chemical forensic analyses is to select a panel of diagnostic chemical featurescolloquially termed a chemical fingerprintthat can predict the presence of a source in a novel sample. However, most of the developed chemical fingerprinting workflows are qualitative in nature. Herein, we report on a quantitative machine learning workflow. Grab samples (n = 51) were collected from five chemical sources, including agricultural runoff, headwaters, livestock manure, (sub)urban runoff, and municipal wastewater. Support vector classification was used to select the top 10, 25, 50, and 100 chemical features that best discriminate each source from all others. The cross-validation balanced accuracy was 92− 100% for all sources (n = 1,000 iterations). When screening for diagnostic features from each source in samples collected from four local creeks, presence probabilities were low for all sources, except for wastewater at two downstream locations in a single creek. Upon closer investigation, a wastewater treatment facility was located ∼3 km upstream of the nearest sample location. In addition, using simulated in silico mixtures, the workflow can distinguish presence and absence of some sources at 10,000-fold dilutions. These results strongly suggest that this workflow can select diagnostic subsets of chemical features that can be used to quantitatively predict the presence/absence of various sources at trace levels in the environment.

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Section: ■ Results and Discussionmentioning

confidence: 92%

Machine Learning Applications for Chemical Fingerprinting and Environmental Source Tracking Using Non-target Chemical Data

Dávila-Santiago

Shi

Mahadwar

et al. 2022

Environ. Sci. Technol.

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“… Filter methods are based only on the intrinsic properties of the data (Solorio-Fernández et al, 2020). Filter method computes an importance value between one independent variable and the dependent variable.…”

Section: Feature Selection Techniquesmentioning

confidence: 99%

A surrogate model based on feature selection techniques and regression learners to improve soybean yield prediction in southern France

Corrales

Schoving

Raynal

et al. 2022

Computers and Electronics in Agriculture

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Empirical and process-based models are currently used to predict crop yield at field and regional levels. A mechanistic model named STICS (Multidisciplinary Simulator for Standard Crops) has been used to simulate soybean grain yield in several environments, including southern France. STICS simulates at a daily step the effects of climate, soil and management practices on plant growth, development and production. In spite of good performances to predict total aboveground biomass, poor results were obtained for final grain yield. In order to improve yield prediction, a surrogate model was developed from STICS dynamic simulations, feature selection techniques and regression learners. STICS was used to simulate functional variables at given growth stages and over selected phenological phases. The most representative variables were selected through feature selection techniques (filter, wrapper and embedded), and a subset of variables were used to train the regression learners Linear regression (LR), Support vector regression (SVR), Back propagation neural network (BPNN), Random forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO) and M5 decision tree. The subset of variables selected by wrapper method combined with regression models SVR (R 2 = 0. 7102; subset of variables = 6) and LR (R 2 = 0. 6912; subset of variables = 14) provided the best results. SVR and LR models improved significantly the soybean yield predictions in southern France in comparison to STICS simulations (R 2 = 0.040).

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“…Hence, some traditional FS methods have received considerable interest due to their ability to evaluate feature importance and select a certain number of top-ranked features. These methods include statistical test (e.g., analysis of variance (ANOVA) [ 8 , 9 ] and Chi-Squared (CHI2) [ 10 , 11 ]), correlation criteria (e.g., Pearson [ 12 ], Spearman [ 13 , 14 ], Kendall [ 15 , 16 ]), and information theory (e.g., symmetrical uncertainty (SU) [ 17 ], mutual information (MI) [ 18 , 19 ], and entropy [ 20 ]). However, the statistical test and correlation criteria techniques only consider the correlation between features and labels, and the feature subsets are not appropriate because some highly correlated but redundant features are selected.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection Based on Adaptive Particle Swarm Optimization with Leadership Learning

et al. 2022

Computational Intelligence and Neuroscience

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With the rapid development of the Internet of Things (IoT), the curse of dimensionality becomes increasingly common. Feature selection (FS) is to eliminate irrelevant and redundant features in the datasets. Particle swarm optimization (PSO) is an efficient metaheuristic algorithm that has been successfully applied to obtain the optimal feature subset with essential information in an acceptable time. However, it is easy to fall into the local optima when dealing with high-dimensional datasets due to constant parameter values and insufficient population diversity. In the paper, an FS method is proposed by utilizing adaptive PSO with leadership learning (APSOLL). An adaptive updating strategy for parameters is used to replace the constant parameters, and the leadership learning strategy is utilized to provide valid population diversity. Experimental results on 10 UCI datasets show that APSOLL has better exploration and exploitation capabilities through comparison with PSO, grey wolf optimizer (GWO), Harris hawks optimization (HHO), flower pollination algorithm (FPA), salp swarm algorithm (SSA), linear PSO (LPSO), and hybrid PSO and differential evolution (HPSO-DE). Moreover, less than 8% of features in the original datasets are selected on average, and the feature subsets are more effective in most cases compared to those generated by 6 traditional FS methods (analysis of variance (ANOVA), Chi-Squared (CHI2), Pearson, Spearman, Kendall, and Mutual Information (MI)).

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A Supervised Filter Feature Selection method for mixed data based on Spectral Feature Selection and Information-theory redundancy analysis

Cited by 31 publications

References 23 publications

Machine Learning Applications for Chemical Fingerprinting and Environmental Source Tracking Using Non-target Chemical Data

Machine Learning Applications for Chemical Fingerprinting and Environmental Source Tracking Using Non-target Chemical Data

A surrogate model based on feature selection techniques and regression learners to improve soybean yield prediction in southern France

Feature Selection Based on Adaptive Particle Swarm Optimization with Leadership Learning

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