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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

Feature Selection in Texts

, 2017
Feature selection is used in many application areas relevant to expert and intelligent systems, such as machine learning, data mining, cheminformatics and natural language processing. In this study we propose methods for feature selection and features analysis based on Support Vector Machines (SVM) with linear kernels.
openaire   +3 more sources

Neuropsychological and Educational Outcomes in Shwachman–Diamond Syndrome—A Report From the North American Shwachman–Diamond Syndrome Registry

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Shwachman–Diamond syndrome (SDS) is a rare autosomal recessive ribosomopathy characterized by bone marrow failure and multisystem involvement, with emerging evidence of associated neurocognitive impairment. Methods We conducted a retrospective study of 240 individuals with biallelic Shwachman–Bodian–Diamond syndrome (SBDS) mutations
Jane Koo, Melissa Gerstle, Leah Cheng, Richard Cooper, Katherine Coyne, Claire Dusa, Naomi E. Joffe, Diana Schwarz, Sarah Steltz, Akiko Shimamura, Kasiani C. Myers, Thea Quinton   +11 more
wiley   +1 more source

Binary Fox Optimization Algorithm for Feature Selection

Cybernetics and Information Technologies
There is an urgent need for algorithms capable of improving the selection of appropriate features that directly affect the improvement process of the algorithm’s accuracy efficiently. Therefore, in this paper, a new feature selection algorithm for binary
Mohammed Athraa Jasim, Ghathwan Khalil Ibrahim, Yusof Yuhanis   +2 more
doaj   +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, Chaeriah Bin Ali Wael, Teguh Praludi   +2 more
doaj   +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

Attack Transferability Against Information-Theoretic Feature Selection

IEEE Access, 2021
Machine learning (ML) is vital to many application-driven fields, such as image and signal classification, cyber-security, and health sciences. Unfortunately, many of these fields can easily have their training data tampered with by an adversary to ...
Srishti Gupta, Roman Golota, Gregory Ditzler   +2 more
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

Survival After Hematopoietic Stem Cell Transplantation in Diamond–Blackfan Anemia Syndrome: The Role of Iron Overload—A Systematic Review

Pediatric Blood &Cancer, EarlyView.
ABSTRACT We assessed the effect of iron overload (IO) on mortality and complications following hematopoietic stem cell transplantation (HSCT) in patients with Diamond–Blackfan anemia syndrome (DBAS) in a systematic review of individual participant data and cohort data from observational studies.
Geoffrey Z. L. Kuppens, Evangelia Kiakou, Lieke de Zwart, Maurizio Miano, Eduard J. van Beers, Marc Bierings, Marije Bartels   +6 more
wiley   +1 more source

Staging of Prostate Cancer Using Automatic Feature Selection, Sampling and Dempster-Shafer Fusion

Cancer Informatics, 2009
A novel technique of automatically selecting the best pairs of features and sampling techniques to predict the stage of prostate cancer is proposed in this study.
Sandeep Chandana, Henry Leung, Kiril Trpkov   +2 more
doaj