The curse of dimensionality is a common challenge in machine learning, and feature selection techniques are commonly employed to address this issue by selecting a subset of relevant features. However, there is no consistently superior approach for choosing the most significant subset of features. We conducted a comprehensive analysis comparing filt...
Published in Journal of multivariate analysis
Forward regression, a classical variable screening method, has been widely used for model building when the number of covariates is relatively low. However, forward regression is seldom used in high-dimensional settings because of the cumbersome computation and unknown theoretical properties. Some recent works have shown that forward regression, co...
Published in BMC Nephrology
BackgroundThe incidence of Acute Kidney Injury (AKI) continues to increase in the UK, with associated mortality rates remaining significant. Approximately one fifth of hospital admissions are associated with AKI and approximately a third of patients with AKI in hospital develop AKI during their time in hospital. A fifth of these cases are considere...
Published in Machine Learning
We present the Parallel, Forward–Backward with Pruning (PFBP) algorithm for feature selection (FS) for Big Data of high dimensionality. PFBP partitions the data matrix both in terms of rows as well as columns. By employing the concepts of p-values of conditional independence tests and meta-analysis techniques, PFBP relies only on computations local...
Published in Quantum Information Processing
Feature selection is a well-known preprocessing technique in machine learning, which can remove irrelevant features to improve the generalization capability of a classifier and reduce training and inference time. However, feature selection is time-consuming, particularly for the applications those have thousands of features, such as image retrieval...
Published in Genetica
Genetic association mapping has been widely applied to determine genetic markers favorably associated with a trait of interest and provide information for marker-assisted selection. Many association mapping studies commonly focus on main effects due to intolerable computing intensity. This study aims to select several sets of DNA markers with poten...
Published in Analytical and Bioanalytical Chemistry
This work presents a new method for forward variable selection and calibration and its evaluation for manganese determination in steel by laser-induced breakdown spectroscopy (LIBS). A compact and low-cost LIBS instrument was used, based on a microchip laser and a grating mini-spectrometer containing a non-intensified, non-gated, and non-cooled lin...
Published in Nonlinear Dynamics
Many kinds of nonlinear engineering structures have nonlinear components which are spatially localized. For these structures, it is usually needed to determine locations and types of nonlinearities firstly to make the subsequent procedure of parameter estimation more efficient and accurate. This paper presents a new approach to identify all the loc...
A forward and backward least angle regression (LAR) algorithm is proposed to construct the nonlinear autoregressive model with exogenous inputs (NARX) that is widely used to describe a large class of nonlinear dynamic systems. The main objective of this paper is to improve model sparsity and generalization performance of the original forward LAR al...
Published in Neural Computing and Applications
MR-based methods have acceded an important role for the clinical detection and diagnosis of breast cancer. Dynamic contrast-enhanced MRI of the breast has become a robust and successful method, especially for the diagnosis of high-risk cases due to its higher sensitivity compared to X-ray mammography. In the clinical setting, the ANN has been widel...
