A design of information granule-based under-sampling method in imbalanced data classification
Sign Up to like & getrecommendations! Published in 2020 at "Soft Computing"
DOI: 10.1007/s00500-020-05023-2
Abstract: In numerous real-world problems, we are faced with difficulties in learning from imbalanced data. The classification performance of a “standard” classifier (learning algorithm) is evidently hindered by the imbalanced distribution of data. The over-sampling and… read more here.
Keywords: information granules; information; imbalanced data; data classification ... See more keywords
Optimization of classification algorithm based on gene expression programming
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Ambient Intelligence and Humanized Computing"
DOI: 10.1007/s12652-017-0563-8
Abstract: Data classification is an important task in the field of data mining, which can be used to mine the model of important data and forecast the future trend of those data. Although some breakthroughs have… read more here.
Keywords: classification algorithm; classification; gep; gene expression ... See more keywords
KNN-based maximum margin and minimum volume hyper-sphere machine for imbalanced data classification
Sign Up to like & getrecommendations! Published in 2019 at "International Journal of Machine Learning and Cybernetics"
DOI: 10.1007/s13042-017-0720-6
Abstract: Imbalanced data classification is often met in our real life. In this paper, a novel k-nearest neighbor (KNN)-based maximum margin and minimum volume hyper-sphere machine (KNN-M3VHM) is presented for the imbalanced data classification. The basic… read more here.
Keywords: machine; imbalanced data; hyper; hyper sphere ... See more keywords
Ensemble RBM-based classifier using fuzzy integral for big data classification
Sign Up to like & getrecommendations! Published in 2019 at "International Journal of Machine Learning and Cybernetics"
DOI: 10.1007/s13042-019-00960-3
Abstract: The restricted Boltzmann machine (RBM) is a primary building block of deep learning models. As an efficient representation learning approach, deep RBM can effectively extract sophisticated and informative features from raw data. Little research has… read more here.
Keywords: rbm; fuzzy integral; data classification; big data ... See more keywords
A novel feature learning framework for high-dimensional data classification
Sign Up to like & getrecommendations! Published in 2021 at "International Journal of Machine Learning and Cybernetics"
DOI: 10.1007/s13042-020-01188-2
Abstract: Feature extraction is an essential component in many classification tasks. Popular feature extraction approaches especially deep learning-based methods, need large training samples to achieve satisfactory performance. Although dictionary learning-based methods are successfully used for feature… read more here.
Keywords: high dimensional; classification; feature; framework ... See more keywords
Developing Transformed Fuzzy Neural Networks to Enhance Medical Data Classification Accuracy
Sign Up to like & getrecommendations! Published in 2018 at "International Journal of Fuzzy Systems"
DOI: 10.1007/s40815-018-0503-6
Abstract: Abstract Developing an automatic model to precisely and quickly classify medical data is a challenging work due to lack of medical knowledge. This paper presented a transformed fuzzy neural network (TFNN) to enhance medical data… read more here.
Keywords: classification; classification accuracy; data classification; transformed fuzzy ... See more keywords
Semisupervised data classification via the Mumford–Shah–Potts-type model
Sign Up to like & getrecommendations! Published in 2017 at "Applied Mathematical Modelling"
DOI: 10.1016/j.apm.2017.05.027
Abstract: Abstract More and more high dimensional data are widely used in many real world applications. This kind of data are obtained from different feature extractors, which represent distinct perspectives of the data. How to classify… read more here.
Keywords: mumford shah; classification; semisupervised data; data classification ... See more keywords
Spectra data classification with kernel extreme learning machine
Sign Up to like & getrecommendations! Published in 2019 at "Chemometrics and Intelligent Laboratory Systems"
DOI: 10.1016/j.chemolab.2019.103815
Abstract: Abstract One of the most important issues in chemometrics is how to design a robust classifier for spectra data with high classification accuracy. Extreme learning machine (ELM) has proved to be an effective method for… read more here.
Keywords: spectra data; learning machine; data classification; classification ... See more keywords
Random vector functional link with ε-insensitive Huber loss function for biomedical data classification
Sign Up to like & getrecommendations! Published in 2022 at "Computer methods and programs in biomedicine"
DOI: 10.1016/j.cmpb.2022.106622
Abstract: BACKGROUND AND OBJECTIVE Biomedical data classification has been a trending topic among researchers during the last decade. Biomedical datasets may contain several features noises. Hence, the conventional machine learning model cannot efficiently handle the presence… read more here.
Keywords: biomedical datasets; biomedical data; function; data classification ... See more keywords
Clinical data classification with noisy intermediate scale quantum computers
Sign Up to like & getrecommendations! Published in 2022 at "Scientific Reports"
DOI: 10.1038/s41598-022-05971-9
Abstract: Quantum machine learning has experienced significant progress in both software and hardware development in the recent years and has emerged as an applicable area of near-term quantum computers. In this work, we investigate the feasibility… read more here.
Keywords: usepackage; data classification; machine learning; quantum ... See more keywords
Polarimetric SAR Data Classification via Reinforcement Learning
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2939232
Abstract: Inspired by human’s learning characteristic that knowledge is gradually learned little by little, a Spatial-Polarimetric Reinforcement Learning (SPRL) approach is proposed for Polarimetric Synthetic Aperture Radar (PolSAR) data classification, from a new perspective of reinforcement… read more here.
Keywords: reinforcement learning; polarimetric sar; data classification; spatial polarimetric ... See more keywords