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   Articles with "feature importance" as a keyword



Comparing Explanations of Molecular Machine Learning Models Generated with Different Methods for the Calculation of Shapley Values

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recommendations! Published in 2025 at "Molecular Informatics"

DOI: 10.1002/minf.202500067

Abstract: Feature attribution methods from explainable artificial intelligence (XAI) provide explanations of machine learning models by quantifying feature importance for predictions of test instances. While features determining individual predictions have frequently been identified in machine learning… read more here.

Keywords: machine; feature importance; learning models; machine learning ... See more keywords

Feature importance feedback with Deep Q process in ensemble-based metaheuristic feature selection algorithms

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recommendations! Published in 2024 at "Scientific Reports"

DOI: 10.1038/s41598-024-53141-w

Abstract: Feature selection is an indispensable aspect of modern machine learning, especially for high-dimensional datasets where overfitting and computational inefficiencies are common concerns. Traditional methods often employ either filter, wrapper, or embedded approaches, which have limitations… read more here.

Keywords: importance feedback; feature; selection; feature importance ... See more keywords

PCaLDI: Explainable Similarity and Distance Metrics Using Principal Component Analysis Loadings for Feature Importance

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recommendations! Published in 2024 at "IEEE Access"

DOI: 10.1109/access.2024.3387547

Abstract: In the evolving landscape of interpretable machine learning (ML) and explainable artificial intelligence, transparent and comprehensible ML models are crucial for data-driven decision-making. Traditional approaches have limitations in distinguishing whether the observed importance of features… read more here.

Keywords: importance; similarity distance; pcaldi; distance metrics ... See more keywords

Heuristic XAI: AIME and HMM Integration Improved-Feature-Importance Assessment Through Heuristics

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recommendations! Published in 2025 at "IEEE Access"

DOI: 10.1109/access.2025.3555626

Abstract: The explainability of AI and machine learning models has become crucial, drawing significant attention to explainable AI (XAI) technologies. Global feature importance extraction is key to understanding the behavior of these models. However, existing XAI… read more here.

Keywords: importance; integration; feature importance; heuristic xai ... See more keywords

A Novel Explainable Deep Belief Network Framework and Its Application for Feature Importance Analysis

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recommendations! 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

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recommendations! 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

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recommendations! 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.

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recommendations! 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

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recommendations! 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

Retracted: Feature Importance Score-Based Functional Link Artificial Neural Networks for Breast Cancer Classification

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recommendations! Published in 2024 at "BioMed Research International"

DOI: 10.1155/2024/9871872

Abstract: [This retracts the article DOI: 10.1155/2022/2696916.]. read more here.

Keywords: based functional; importance score; retracted feature; functional link ... See more keywords

Predicting student performance using Moodle data and machine learning with feature importance

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recommendations! Published in 2025 at "Indonesian Journal of Electrical Engineering and Computer Science"

DOI: 10.11591/ijeecs.v37.i1.pp223-231

Abstract: Despite the growing technological advancement in education, poor academic performance of students remains challenging for educational institutions worldwide. The study aimed to predict students’ academic performance through modular object-oriented dynamic learning environment (Moodle) data and… read more here.

Keywords: performance; feature importance; moodle data; machine learning ... See more keywords