Results 51 to 60 of about 2,877,618 (260)
Utilizing Semantic Textual Similarity for Clinical Survey Data Feature Selection [PDF]
arXiv, 2023 Survey data can contain a high number of features while having a comparatively low quantity of examples. Machine learning models that attempt to predict outcomes from survey data under these conditions can overfit and result in poor generalizability. One remedy to this issue is feature selection, which attempts to select an optimal subset of features ...
arxiv
Non-uniform Feature Sampling for Decision Tree Ensembles
, 2014 We study the effectiveness of non-uniform randomized feature selection in decision tree classification. We experimentally evaluate two feature selection methodologies, based on information extracted from the provided dataset: $(i)$ \emph{leverage scores ...
Kyrillidis, Anastasios +1 more
core +1 more source
Weighted Heuristic Ensemble of Filters [PDF]
, 2015 Feature selection has become increasingly important in data mining in recent years due to the rapid increase in the dimensionality of big data. However, the reliability and consistency of feature selection methods (filters) vary considerably on different
Aldehim, Ghadah, Wang, Wenjia
core +1 more source
Optimal Feature Aggregation and Combination for Two-Dimensional Ensemble Feature Selection
Information, 2020 Feature selection is a way of reducing the features of data such that, when the classification algorithm runs, it produces better accuracy. In general, conventional feature selection is quite unstable when faced with changing data characteristics.
Machmud Roby Alhamidi, Wisnu Jatmiko
doaj +1 more source
AutoEncoder Inspired Unsupervised Feature Selection
, 2018 High-dimensional data in many areas such as computer vision and machine learning tasks brings in computational and analytical difficulty. Feature selection which selects a subset from observed features is a widely used approach for improving performance ...
Han, Kai +4 more
core +1 more source
Feature selection guided by structural information [PDF]
, 2009 In generalized linear regression problems with an abundant number of features, lasso-type regularization which imposes an $\ell^1$-constraint on the regression coefficients has become a widely established technique.
Castell, Wolfgang zu +2 more
core +2 more sources
Causal Feature Selection [PDF]
, 2007 This chapter reviews techniques for learning causal relationships from data, in application to the problem of feature selection. Most feature selection methods do not attempt to uncover causal relationships between feature and target and focus instead on making best predictions.
Constantin F. Aliferis +2 more
openaire +1 more source
Infinite Latent Feature Selection Technique for Hyperspectral Image Classification
Jurnal Elektronika dan Telekomunikasi, 2019 The classification process is one of the most crucial processes in hyperspectral imaging. One of the limitations in classification process using machine learning technique is its complexities, where hyperspectral image format has a thousand band that can
Tajul Miftahushudur +2 more
doaj +1 more source
Features of Selective Kinase Inhibitors [PDF]
Chemistry & Biology, 2005 Small-molecule inhibitors of protein and lipid kinases have emerged as indispensable tools for studying signal transduction. Despite the widespread use of these reagents, there is little consensus about the biochemical criteria that define their potency and selectivity in cells.
Kevan M. Shokat +2 more
openaire +3 more sources
Entropy-based feature selection with applications to industrial internet of things (IoT) and breast cancer prediction [PDF]
Big Data and Computing VisionsFeature Selection (FS) is employed in the Machine Learning (ML) process to increase accuracy. Eliminating redundant and irrelevant variables while keeping the most important ones boosts the prediction capacity of the algorithms.
Ismail Mageed
doaj +1 more source