Results 51 to 60 of about 7,571,754 (304)
Pairwise meta-rules for better meta-learning-based algorithm ranking [PDF]
, 2013 In this paper, we present a novel meta-feature generation method in the context of meta-learning, which is based on rules that compare the performance of individual base learners in a one-against-one manner.
Pfahringer, Bernhard, Sun, Quan
core +1 more source
Decision Support System for Medical Diagnosis Utilizing Imbalanced Clinical Data
Applied Sciences, 2018 The clinical decision support system provides an automatic diagnosis of human diseases using machine learning techniques to analyze features of patients and classify patients according to different diseases.
Huirui Han +3 more
doaj +1 more source
Modelling stem cell differentiation related processes—A practical overview for biologists
FEBS Letters, EarlyView.Stem cell differentiation is complex and difficult to control experimentally. This review introduces suitable computational modelling approaches that can support stem cell research, from mechanistic ODE and abstract models to multiscale and deep learning methods.
Ricco Zeegelaar +4 more
wiley +1 more source
Multi-Label Classification Algorithm Based on Embedded Feature Extraction [PDF]
Jisuanji gongcheng, 2019 Dimensionality reduction and feature selection methods based on single-label classification cannot be directly applied to multi-label learning.If a multi-label learning problem is composed into multiple independent single-label learning problems to ...
WANG Xiaoying, XIE Jun, TAO Xingliu, SHAO Dongsheng, WANG Zhong
doaj +1 more source
Design and analysis strategies for robust microbiome ageing research
FEBS Letters, EarlyView.The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik +5 more
wiley +1 more source
Minimal learning machine for multi-label learning
Machine LearningAbstract Distance-based supervised method, the minimal learning machine, constructs a predictive model from data by learning a mapping between input and output distance matrices. In this paper, we propose new methods and evaluate how their core component, the distance mapping, can be adapted to multi-label learning.
Joonas Hämäläinen +5 more
openaire +5 more sources
Multi-Label Learning With Hidden Labels [PDF]
IEEE Access, 2020 In multi-label learning, each object is represented by a single instance and associated with multiple labels simultaneously. Existing multi-label learning approaches mainly construct classification models with a fixed set of target labels (observed labels).
Jun Huang 0003 +5 more
openaire +3 more sources
Microbiome‐blood–brain barrier interactions in aging — mechanisms and therapeutic potential
FEBS Letters, EarlyView.Aging reshapes the gut microbiome (↓SCFA‐producing commensals; ↑pro‐inflammatory outputs), shifting circulating metabolites (↓SCFAs; ↑LPS, ↑TMAO, ↑PAA) that act at the BBB to increase nonspecific transcytosis, alter transport, and promote astrocyte reactivity, heightening brain vulnerability.
Daniel Cuervo‐Zanatta +3 more
wiley +1 more source
Comparing Multi-Label Classification Methods for Provisional Biopharmaceutics Class Prediction. [PDF]
, 2014 The biopharmaceutical classification system (BCS) is now well established and utilized for the development and biowaivers of immediate oral dosage forms. The prediction of BCS class can be carried out using multilabel classification.
Taravat Ghafourian +8 more
core +1 more source
Deep active learning for multi label text classification
Scientific ReportsGiven a set of labels, multi-label text classification (MLTC) aims to assign multiple relevant labels for a text. Recently, deep learning models get inspiring results in MLTC.
Qunbo Wang +5 more
doaj +1 more source