Article
Mathematics, Applied
H. Ribera, S. Shirman, A. Nguyen, N. M. Mangan
Summary: Many natural systems exhibit chaotic behavior, but characterizing these systems can be challenging due to sensitivity to initial conditions and difficulties in differentiating chaotic behavior from noise. We present a method combining variational annealing with sparse-optimization methods to perform model identification for chaotic systems with unmeasured variables.
Article
Mathematics, Applied
Sheng Fang, Yong-Jin Liu, Xianzhu Xiong
Summary: This paper introduces efficient algorithms for solving the Dantzig selector problem, which demonstrate global and local convergence under mild conditions, along with reduced computational costs by utilizing second order sparsity and efficient techniques.
SIAM JOURNAL ON SCIENTIFIC COMPUTING
(2021)
Article
Mathematics, Applied
Huanmin Ge, Peng Li
Summary: In this paper, we propose the Dantzig selector based on l(1)-alpha l(2) minimization for signal recovery. The proposed model and algorithm demonstrate better performance in signal recovery compared to existing methods, as shown by extensive numerical experiments.
Article
Mathematics, Applied
Swapnil Das, James Demmel, Kimon Fountoulakis, Laura Grigori, Michael W. Mahoney, Shenghao Yang
Summary: This study focuses on parallelizing the LARS algorithm for linear regression with two different versions: bLARS and tournament-bLARS (T-bLARS). These algorithms have different speedup and output accuracy, reducing arithmetic operations and improving efficiency. Numerous numerical experiments demonstrate up to 4x speedups compared to the traditional LARS algorithm without sacrificing solution quality.
SIAM JOURNAL ON SCIENTIFIC COMPUTING
(2021)
Article
Chemistry, Multidisciplinary
Mingxing Li, Hongzheng Sun, Fredrick Oteng Agyeman, Mohammad Heydari, Arif Jameel, Hira Salah ud din Khan
Summary: Through empirical analysis, the study identifies key factors affecting China's economic growth, including consumption levels, development of the tertiary industry, financial development, and industrialization. These factors have varying degrees of impact on promoting economic growth in China.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Information Systems
Manoj Sharma, Naresh Kumar, Vijay Pal Singh, Charanjeet Madan, Sandeep Sarowa
Summary: This study proposes an intelligent framework for the categorization of darknet traffic, using a hybrid feature selector. The experimental results demonstrate that the proposed XGBoost-HLRF model performs well in the classification of darknet traffic.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Green & Sustainable Science & Technology
Dong Li, Xiaofei Ren
Summary: Accurate carbon price prediction is crucial for governments to improve the environment and reduce carbon emissions more cost-effectively. Many scholars have tried to predict carbon prices using multiple factors, but this approach has increased complexity and decreased interpretability. To address these issues, we propose a carbon price prediction model based on LsOALEO feature selection and time-delay least angle regression (LsOALEO-FSTDLARS). Experimental results demonstrate that this model outperforms other prediction methods in terms of accuracy, generalization ability, and stability.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Biochemical Research Methods
Ayyuce Begum Bektas, Cigdem Ak, Mehmet Gonen
Summary: With the increasing sizes of computational biology datasets, previous kernel-based machine learning algorithms have failed to provide satisfactory interpretability. To address this issue, we propose a fast and efficient multiple kernel learning algorithm that can extract significant information from genomic data. Our experiments demonstrate that the algorithm outperforms baseline methods while using only a small fraction of input features, and it has the potential to discover new biomarkers and therapeutic guidelines.
Article
Computer Science, Interdisciplinary Applications
Jasleen Kaur Sethi, Mamta Mittal
Summary: This research investigates the effectiveness of a feature selection method based on LASSO for predicting air quality in Delhi and surrounding cities, identifying meteorological factors and pollutant concentrations as the most important influencing factors, and suggesting preventive measures to improve air quality.
EARTH SCIENCE INFORMATICS
(2021)
Article
Mathematics
Zhongzheng Wang, Guangming Deng, Jianqi Yu
Summary: The proposed group screening procedure based on the information gain ratio for a classification model is shown to have better screening performance and classification accuracy.
JOURNAL OF MATHEMATICS
(2022)
Article
Automation & Control Systems
Louna Alsouki, Laurent Duval, Clement Marteau, Rami El Haddad, Francois Wahl
Summary: Relating variables X to response y is important in chemometrics. Qualitative interpretation can enhance quantitative prediction by identifying influential features. Projections (e.g. PLS) and variable selections (e.g. lasso) are used for dimension reduction in high-dimensional problems. Dual-sPLS, a variant of PLS1, provides a balance between accurate prediction and efficient interpretation through penalizations inspired by classical regression methods and the dual norm notion. It performs favorably compared to similar regression methods on simulated and real chemical data.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2023)
Article
Biochemical Research Methods
Akio Onogi, Aisaku Arakawa
Summary: An R package has been developed to implement multiple linear learners in a single model. It uses fast algorithms to obtain solutions and is useful for incorporating multimodal and high-dimensional explanatory variables in regression models.
Article
Agronomy
Bin Li, Yuqi Wang, Lisha Li, Yande Liu
Summary: Machine learning is widely used in near-infrared spectroscopy (NIRS) for fruit classification. A classification instance selection method based on the least-angle regression (LAR) was proposed to compress the sample size while improving accuracy, and experimental results supported its effectiveness.
Article
Biochemistry & Molecular Biology
William E. Gilbraith, J. Chance Carter, Kristl L. Adams, Karl S. Booksh, Joshua M. Ottaway
Summary: The study presents four unique prediction techniques and various data pre-processing methods to analyze different oil types and peroxide values, incorporating natural aging effects. By utilizing near-infrared spectra and different regression analysis methods, prediction models were established, showing promising advancements in developing a global model for determining peroxide values in edible oils.
Article
Computer Science, Information Systems
Sanush K. Abeysekera, Ye-Chow Kuang, Melanie Po-Leen Ooi, Vineetha Kalavally
Summary: This paper introduces the Maximal Associated Regression (MAR) and its monotonically constrained extension (MARm) algorithms, which use nonlinear transformations to fit predictor variables, effectively generating sparse models, suitable for subset selection and data exploration.
Article
Statistics & Probability
Yingying Fan, Gareth M. James, Peter Radchenk
ANNALS OF STATISTICS
(2015)
Article
Statistics & Probability
Peter Radchenko
JOURNAL OF MULTIVARIATE ANALYSIS
(2015)
Article
Statistics & Probability
Peter Radchenko, Xinghao Qiao, Gareth M. James
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2015)
Article
Statistics & Probability
Peter Radchenko, Gourab Mukherjee
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
(2017)
Article
Computer Science, Information Systems
Rahul Mazumder, Peter Radchenko
IEEE TRANSACTIONS ON INFORMATION THEORY
(2017)
Article
Statistics & Probability
Trambak Banerjee, Gourab Mukherjee, Peter Radchenko
JOURNAL OF MULTIVARIATE ANALYSIS
(2017)
Article
Statistics & Probability
Peter Radchenko, Gareth M. James
ANNALS OF APPLIED STATISTICS
(2011)
Article
Statistics & Probability
Peter Radchenko
ANNALS OF STATISTICS
(2008)
Article
Biology
Gareth M. James, Peter Radchenko
Article
Statistics & Probability
Peter Radchenko, Gareth M. James
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2008)
Article
Statistics & Probability
Peter Radchenko, Gareth M. James
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2010)
Article
Statistics & Probability
Gareth M. James, Peter Radchenko, Bradley Rava
Summary: The paper presents an empirical Bayes approach called ECAP, which corrects for selection bias in probability estimates using a variant of Tweedie's formula. The method is flexible and does not rely on restrictive assumptions about prior probabilities. The authors demonstrate through theoretical analysis and real-world datasets that ECAP can significantly improve upon the original probability estimates.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2022)
Article
Mathematics, Applied
Nick James, Max Menzies, Peter Radchenko
Summary: This paper introduces new methods to analyze the changing progression of COVID-19 cases to deaths in different waves of the pandemic, finding significant heterogeneity in mortality rate reduction among European countries and U.S. states.
Article
Statistics & Probability
D Pollard, P Radchenko
JOURNAL OF MULTIVARIATE ANALYSIS
(2006)