Learning with continuous piecewise linear decision trees

Tao, Qinghua; Zhen, Li; Xu, Jun; Xie, Na; Wang, Shuning; Suykens, Johan A. K.

doi:10.1016/j.eswa.2020.114214

Cited by 11 publications

(6 citation statements)

References 30 publications

(34 reference statements)

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“…It considers all the data in the initial stage and divides it into branches. Each branch indicates a condition of input values, and the branch grows like a tree based on the relevant conditions and threshold value until the best value is found . However, as being the result of fitting to a single tree, a DT model is prone to overfitting, and achieving generalized optimal tree depth is challenging …”

Section: Methodsmentioning

confidence: 99%

“…Each branch indicates a condition of input values, and the branch grows like a tree based on the relevant conditions and threshold value until the best value is found. 58 However, as being the result of fitting to a single tree, a DT model is prone to overfitting, 41 and achieving generalized optimal tree depth is challenging. 59 In contrast, RF is an ensemble learning model�it learns from the ensemble of decision trees.…”

Section: Machine Learning Model Trainingmentioning

confidence: 99%

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A Machine Learning Model for Predicting Composition of Catalytic Coprocessing Products from Molecular Beam Mass Spectra

Jabed

Kim

Clark

et al. 2023

ACS Sustainable Chem. Eng.

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Demand for the development of an automated and integrated refining process for biofuels has increased in recent years due to the lack of generalized process inspection tools. In bio-oil upgrading processes, all process variables are maintained based on the offline specification of intermediates and products. A lack of real-time product specifications in batch-wise monitoring can cause process failure and wasted resources. Therefore, there is a need for a fast and accurate intermediates/product specification tool that can be used for real-time specification to reduce waste and mitigate the risk of process failure. To address this gap, we developed a machine learning (ML) model for predicting speciated bio-oil composition, including paraffin, iso-paraffins, olefins, naphthene, and aromatics. The model is trained using the mass spectra from upgraded products collected in the vapor phase before condensation and predicts the composition of the condensed product. Training ML models using raw mass spectra is challenging due to numerous overlapped peaks originating from different parent compounds. With this in mind, we propose a protocol that (i) transforms raw mass spectra to chemistry-inspired predefined features and (ii) trains decision tree-based models using these features. Our results show that the random forest model was robust against overfitting and had the highest accuracy compared to other models. Moreover, a stochastic ablation method determined the eight most significant features while maximizing the accuracy. Our protocol facilitates real-time compositional analysis of upgraded bio-oils and thus real-time process monitoring. Additionally, this protocol enables the rational design of efficient catalysts and the determination of optimal process conditions.

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Section: Methodsmentioning

confidence: 99%

Section: Machine Learning Model Trainingmentioning

confidence: 99%

A Machine Learning Model for Predicting Composition of Catalytic Coprocessing Products from Molecular Beam Mass Spectra

Jabed

Kim

Clark

et al. 2023

ACS Sustainable Chem. Eng.

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“…In the growing process of DTs, by extending the tree with multi leaf nodes and specifying a decision rule to describe the model output of the instances in the corresponding subregion, the domain is recursively divided into several subregions. A complete prediction model was formed by the linear combination of these decision rules at the leaf nodes [31].…”

Section: Model Constructionmentioning

confidence: 99%

Feature Wavelength Selection Based on the Combination of Image and Spectrum for Aflatoxin B1 Concentration Classification in Single Maize Kernels

2022

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Aflatoxin B1 (AFB1) is a very strong carcinogen, maize kernels are easily infected by this toxin during storage. Rapid and accurate identification of AFB1 is of great significance to ensure food safety. In this study, a novel method for classification of AFB1 in single maize kernels was developed. Four groups of maize kernel samples with different AFB1 concentrations (10, 20, 50, and 100 ppb) were prepared by artificial inoculation of toxin. In addition, one group of maize kernel samples without AFB1 were prepared as control, each group with 70 samples. The visible and short wave near-infrared (Vis-SWNIR) region (500–1000 nm) and long wave near-infrared (LWNIR) region (1000–2000 nm) hyperspectral images of all samples were obtained respectively, and the hyperspectral images in 500–2000 nm range was obtained after spectral pretreatment and fusion. Kennard-Stone algorithm was used to divide the samples into calibration set or prediction set. Competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to roughly select the characteristic wavelengths of the calibration set samples, and 25 and 26 effective wavelengths were obtained respectively. Based on the roughly selected wavelengths, a method of fine selection of the characteristic wavelengths was proposed by using the gray-value difference of image (GDI), and a few number of characteristic wavelengths were further selected. Under the LDA classification model, 10 characteristic wavelengths were selected to test the prediction set and the independent verification samples, and the ideal result were obtained with an accuracy of 94.46% and 91.11%, respectively. This study provides a new approach for AFB1 concentration classification of single maize kernels.

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“…However, these local linear regressions bring substantially higher computational burden. A novel PWL decision tree as a flexible and efficient alternative has been previously constructed 89 , which can be regarded as the extension of ReLU to the learning framework of decision trees.…”

Section: Tree Searching Algorithmmentioning

confidence: 99%