Results 41 to 50 of about 642,813 (313)

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, Chaeriah Bin Ali Wael, Teguh Praludi   +2 more
doaj   +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

Geographic variation in walking activity in the red flour beetle Tribolium castaneum

Population Ecology, EarlyView.
This study examined whether there is geographic variation in field populations, focusing on the moving activity in the red flour beetle Tribolium castaneum. Results showed significant differences in moving activity among field populations but no correlation with latitude or meteorological factors.
Kentarou Matsumura
wiley   +1 more source

Entropy-based feature selection with applications to industrial internet of things (IoT) and breast cancer prediction [PDF]

Big Data and Computing Visions
Feature 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

occumb: An R package for site occupancy modeling of eDNA metabarcoding data

Population Ecology, EarlyView.
This study introduces a new R package, occumb, for the convenient application of site occupancy modeling using environmental DNA (eDNA) metabarcoding data. We outline a data analysis workflow, including data setup, model fitting, model assessment, and comparison of potential study settings based on model predictions, all of which can be performed using
Keiichi Fukaya, Yuta Hasebe
wiley   +1 more source

Feature Redundancy Based on Interaction Information for Multi-Label Feature Selection

IEEE Access, 2020
Recent years, multi-label feature selection has gradually attracted significant attentions from machine learning, statistical computing and related communities and has been widely applied to diverse problems from music recognition to text mining, image ...
Wanfu Gao, Juncheng Hu, Yonghao Li, Ping Zhang   +3 more
doaj   +1 more source

Feature Selection for Portfolio Optimization [PDF]

SSRN Electronic Journal, 2015
Most portfolio selection rules based on the sample mean and covariance matrix perform poorly out-of-sample. Moreover, there is a growing body of evidence that such optimization rules are not able to beat simple rules of thumb, such as 1/N. Parameter uncertainty has been identified as one major reason for these findings. A strand of literature addresses
Alex Weissensteiner, Alex Weissensteiner, Thomas Trier Bjerring, Omri Ross, Omri Ross   +4 more
openaire   +6 more sources

The dual nature of TDC – bridging dendritic and T cells in immunity

FEBS Letters, EarlyView.
TDC are hematopoietic cells combining dendritic and T cell features. They reach secondary lymphoid organs (SLOs) and peripheral organs (liver and lungs) after FLT3‐dependent development in the bone marrow and maturation in the thymus. TDC are activated and enriched in SLOs upon viral infection, suggesting that they might play unique immune roles, since
Maria Nelli, Mirela Kuka
wiley   +1 more source

Hybrid feature selection based ScC and forward selection methods [PDF]

International Journal of Data and Network Science
Operational data is always huge. A preprocessing step is needed to prepare such data for the analytical process so the process will be fast. One way is by choosing the most effective features and removing the others. Feature selection algorithms
Luai Al-Shalabi
doaj   +1 more source

Feature Selection by Reordering

, 2005
Feature selection serves for both reduction of the total amount of available data (removing of valueless data) and improvement of the whole behavior of a given induction algorithm (removing data that cause deterioration of the results). A method of proper selection of features for an inductive algorithm is discussed.
Jiřina, M. (Marcel), Jiřina jr., M.
openaire   +4 more sources