Computational Methods of Feature Selection, Huan Liu, Hiroshi Motoda, CRC Press, Boca Raton, FL (2007). 440 pp., Price: $93.95, ISBN: 978-1-58488-878-9This book will be of interest to researchers who already hav
Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance Feature selection across diverse features in molecular systems is challenging. Here, the authors present Differentiable Information Imbalance (DII), a method to optimize feature weights, align units, and ...
Traditional gene predicting methods could performance well when predicting gene sequences with fixed lengths. However, when it comes to gene sequences with large length variations, such methods may lose effectiveness or even feasibility. This might be because the constant dimension of feature space use...
Feature selection methods, crucial for identifying pertinent features in a dataset, fall into three categories: wrapper, embedded, and filter techniques (see Fig.7) [12]. Embedded methods, like most minor absolute shrinkage and selection operator (LASSO) regression and ridge regression (RR), embed...
Figure 1. Schematic Workflow of Matrix Completion-Based Methods Three matrices (including the lncRNA-disease association matrix, lncRNA-lncRNA matrix, and disease-disease matrix) were first obtained as the input data. Then, feature extraction was accomplished based on the above three matrices to ob...
We obtain a large feature space of dimension 1424 which is quite likely to be over-fitted. To solve this issue, we perform feature selection algorithm called SVM-RFE (support vector machine-recursive feature elimination) on each feature group, and also on the combined feature space. By this ...
relative positions in the sequence. Because the feature space explored was very large, FGA iteratively reduced the size of the feature set by eliminating features according to various feature-selection methods. Then, the final set of features that we obtained became input for the learning algorithm...
The selection of the initial values in gradient-based algorithms is critical, because they easily tend to fall to local minimums. This feature is escalated in multi-objective optimization like constitutive parameter characterization. However, in the vicinity of the solution, the convergence is fast ...
advanced field ionization methods (DOI:10.1103/PhysRevA.59.569,LV Keldysh, BSI) Besides the electro-magnetic PIC algorithm and extensions to it, we developed a wide range of tools and diagnostics, e.g.: online, far-field radiation diagnostics for coherent and incoherent radiation emitted by charge...
Multi-layer feature selection incorporating weighted score-based expert knowledge toward modeling materials with targeted properties. Adv. Theory Simul. 3, 1900215 (2020). Article CAS Google Scholar Mangal, A. & Holm, E. A. A comparative study of feature selection methods for stress hotspot ...