Deep supervised learning with mixture of neural networks
Sign Up to like & getrecommendations! Published in 2020 at "Artificial intelligence in medicine"
DOI: 10.1016/j.artmed.2019.101764
Abstract: Deep Neural Network (DNN), as a deep architectures, has shown excellent performance in classification tasks. However, when the data has different distributions or contains some latent non-observed factors, it is difficult for DNN to train… read more here.
Keywords: classification; network; deep supervised; mixture ... See more keywords
Multivariate feature ranking with high-dimensional data for classification tasks
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3180773
Abstract: In many machine learning classification problems, datasets are usually of high dimensionality and therefore require efficient and effective methods for identifying the relative importance of their attributes, eliminating the redundant and irrelevant ones. Due to… read more here.
Keywords: feature; feature ranking; ranking methods; multivariate feature ... See more keywords
DLTA: A Framework for Dynamic Crowdsourcing Classification Tasks
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2018.2849385
Abstract: The increasing popularity of crowdsourcing markets enables the application of crowdsourcing classification tasks. How to conduct quality control in such an application to achieve accurate classification results from noisy workers is an important and challenging… read more here.
Keywords: dlta framework; classification tasks; budget allocation; crowdsourcing classification ... See more keywords