A Novel Explainable Deep Belief Network Framework and Its Application for Feature Importance Analysis
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Sensors Journal"
DOI: 10.1109/jsen.2021.3084846
Abstract: Feature analysis and selection are highly considered topics in deep learning (DL) real-world applications. However, most existing methods are manual and lack of deep insights of training mechanisms. This is because DL is often viewed… read more here.
Keywords: importance; deep belief; analysis; feature importance ... See more keywords
Superpixel-Based Active Learning and Online Feature Importance Learning for Hyperspectral Image Analysis
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2016.2609404
Abstract: The rapid development of multichannel optical imaging sensors has led to increased utilization of hyperspectral data for remote sensing. For classification of hyperspectral data, an informative training set is necessary for ensuring robust performance. However,… read more here.
Keywords: active learning; image; feature importance; remote sensing ... See more keywords
Efficient Sample and Feature Importance Mining in Semi-Supervised EEG Emotion Recognition
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Circuits and Systems II: Express Briefs"
DOI: 10.1109/tcsii.2022.3163141
Abstract: Recently, electroencephalogram (EEG)-based emotion recognition has attracted increasing interests in research community. The weak, non-stationary, multi-rhythm and multi-channel properties of EEG data easily cause the extracted EEG samples and features contribute differently in recognizing emotional… read more here.
Keywords: feature importance; importance; feature; sample feature ... See more keywords
Residual Sketch Learning for a Feature-Importance-Based and Linguistically Interpretable Ensemble Classifier.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2023.3242049
Abstract: Motivated by both the commonly used "from wholly coarse to locally fine" cognitive behavior and the recent finding that simple yet interpretable linear regression model should be a basic component of a classifier, a novel… read more here.
Keywords: importance based; feature importance; residual sketch; ensemble classifier ... See more keywords
The Impact of Feature Importance Methods on the Interpretation of Defect Classifiers
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Software Engineering"
DOI: 10.1109/tse.2021.3056941
Abstract: Classifier specific (CS) and classifier agnostic (CA) feature importance methods are widely used (often interchangeably) by prior studies to derive feature importance ranks from a defect classifier. However, different feature importance methods are likely to… read more here.
Keywords: computed feature; feature importance; importance methods; importance ... See more keywords
Consistency of Feature Importance Algorithms for Interpretable EEG Abnormality Detection
Sign Up to like & getrecommendations! Published in 2022 at "Studies in health technology and informatics"
DOI: 10.3233/shti220801
Abstract: Recent advances in machine learning show great potential for automatic detection of abnormalities in electroencephalography (EEG). While simple and interpretable models combined with expert-comprehensible input features offer full control of the decision making process, these… read more here.
Keywords: feature importance; feature; importance; consistency feature ... See more keywords