Improved prediction of soil properties with Multi-target Stacked Generalisation on EDXRF spectra

• URL: http://arxiv.org/abs/2002.04312v1
• Date: Tue, 11 Feb 2020 11:05:03 GMT
• Title: Improved prediction of soil properties with Multi-target Stacked Generalisation on EDXRF spectra
• Authors: Everton Jose Santana and Felipe Rodrigues dos Santos and Saulo Martiello Mastelini and Fabio Luiz Melquiades and Sylvio Barbon Jr
• Abstract summary: Energy dispersive X-ray fluorescence (EDXRF) is one of the more quick, environmentally friendly and less expensive analytical methods. Some challenges in EDXRF spectral data analysis still demand more efficient methods capable of providing accurate outcomes. Using Multi-target Regression (MTR) methods, multiple parameters can be predicted, and also taking advantage of inter-correlated parameters the overall predictive performance can be improved.
• Score: 1.2599533416395765
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine Learning (ML) algorithms have been used for assessing soil quality parameters along with non-destructive methodologies. Among spectroscopic analytical methodologies, energy dispersive X-ray fluorescence (EDXRF) is one of the more quick, environmentally friendly and less expensive when compared to conventional methods. However, some challenges in EDXRF spectral data analysis still demand more efficient methods capable of providing accurate outcomes. Using Multi-target Regression (MTR) methods, multiple parameters can be predicted, and also taking advantage of inter-correlated parameters the overall predictive performance can be improved. In this study, we proposed the Multi-target Stacked Generalisation (MTSG), a novel MTR method relying on learning from different regressors arranged in stacking structure for a boosted outcome. We compared MTSG and 5 MTR methods for predicting 10 parameters of soil fertility. Random Forest and Support Vector Machine (with linear and radial kernels) were used as learning algorithms embedded into each MTR method. Results showed the superiority of MTR methods over the Single-target Regression (the traditional ML method), reducing the predictive error for 5 parameters. Particularly, MTSG obtained the lowest error for phosphorus, total organic carbon and cation exchange capacity. When observing the relative performance of Support Vector Machine with a radial kernel, the prediction of base saturation percentage was improved in 19%. Finally, the proposed method was able to reduce the average error from 0.67 (single-target) to 0.64 analysing all targets, representing a global improvement of 4.48%.

Related papers

• A Gradient Analysis Framework for Rewarding Good and Penalizing Bad Examples in Language Models [63.949883238901414]
  We present a unique angle of gradient analysis of loss functions that simultaneously reward good examples and penalize bad ones in LMs. We find that ExMATE serves as a superior surrogate for MLE, and that combining DPO with ExMATE instead of MLE further enhances both the statistical (5-7%) and generative (+18% win rate) performance.
  arXiv  Detail & Related papers  (2024-08-29T17:46:18Z)
• Distribution Learning for Molecular Regression [10.96062816455682]
  Distributional Mixture of Experts (DMoE) is a model-independent, and data-independent method for regression. We evaluate the performance of DMoE on different molecular property prediction datasets.
  arXiv  Detail & Related papers  (2024-07-30T00:21:51Z)
• DeepMpMRI: Tensor-decomposition Regularized Learning for Fast and High-Fidelity Multi-Parametric Microstructural MR Imaging [15.408939800451696]
  This paper proposes a unified framework for fast and high-fidelity multi-parametric estimation from various diffusion models. DeepMpMRI is equipped with a newly designed tensor-decomposition-based regularizer to effectively capture fine details.
  arXiv  Detail & Related papers  (2024-05-06T04:36:02Z)
• Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms [88.74308282658133]
  Reization (RP) Policy Gradient Methods (PGMs) have been widely adopted for continuous control tasks in robotics and computer graphics. Recent studies have revealed that, when applied to long-term reinforcement learning problems, model-based RP PGMs may experience chaotic and non-smooth optimization landscapes. We propose a spectral normalization method to mitigate the exploding variance issue caused by long model unrolls.
  arXiv  Detail & Related papers  (2023-10-30T18:43:21Z)
• Compound Batch Normalization for Long-tailed Image Classification [77.42829178064807]
  We propose a compound batch normalization method based on a Gaussian mixture. It can model the feature space more comprehensively and reduce the dominance of head classes. The proposed method outperforms existing methods on long-tailed image classification.
  arXiv  Detail & Related papers  (2022-12-02T07:31:39Z)
• Optimization of Annealed Importance Sampling Hyperparameters [77.34726150561087]
  Annealed Importance Sampling (AIS) is a popular algorithm used to estimates the intractable marginal likelihood of deep generative models. We present a parameteric AIS process with flexible intermediary distributions and optimize the bridging distributions to use fewer number of steps for sampling. We assess the performance of our optimized AIS for marginal likelihood estimation of deep generative models and compare it to other estimators.
  arXiv  Detail & Related papers  (2022-09-27T07:58:25Z)
• Sparse high-dimensional linear regression with a partitioned empirical Bayes ECM algorithm [62.997667081978825]
  We propose a computationally efficient and powerful Bayesian approach for sparse high-dimensional linear regression. Minimal prior assumptions on the parameters are used through the use of plug-in empirical Bayes estimates. The proposed approach is implemented in the R package probe.
  arXiv  Detail & Related papers  (2022-09-16T19:15:50Z)
• Highly efficient reliability analysis of anisotropic heterogeneous slopes: Machine Learning aided Monte Carlo method [0.0]
  This paper presents a highly efficient Machine Learning aided reliability technique. It is able to accurately predict the results of a Monte Carlo (MC) reliability study, and yet performs 500 times faster. The proposed technique reduces the computational time required for our study from 306 days to only 14 hours, providing 500 times higher efficiency.
  arXiv  Detail & Related papers  (2022-04-04T16:28:53Z)
• Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Non-uniform Subsampling of Gradients [54.90670513852325]
  We propose a non-uniform subsampling scheme to improve the sampling accuracy. EWSG is designed so that a non-uniform gradient-MCMC method mimics the statistical behavior of a batch-gradient-MCMC method. In our practical implementation of EWSG, the non-uniform subsampling is performed efficiently via a Metropolis-Hastings chain on the data index.
  arXiv  Detail & Related papers  (2020-02-20T18:56:18Z)
• Double/Debiased Machine Learning for Treatment and Causal Parameters [5.405360145866329]
  We show how to remove the regularization bias by solving auxiliary prediction problems via ML tools. The resulting method could be called a "double ML" method because it relies on estimating primary and auxiliary predictive models. This allows us to use a very broad set of ML predictive methods in solving the auxiliary and main prediction problems.
  arXiv  Detail & Related papers  (2016-07-30T01:58:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.