Feature selection ≈ Feature selection
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mixOmics: An R package for ‘omics feature selection and multiple data integration Open
The advent of high throughput technologies has led to a wealth of publicly available 'omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to …
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Feature Selection Open
Feature selection, as a data preprocessing strategy, has been proven to be effective and efficient in preparing data (especially high-dimensional data) for various data-mining and machine-learning problems. The objectives of feature select…
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An Overview of Overfitting and its Solutions Open
Overfitting is a fundamental issue in supervised machine learning which prevents us from perfectly generalizing the models to well fit observed data on training data, as well as unseen data on testing set. Because of the presence of noise,…
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A Survey on Evolutionary Computation Approaches to Feature Selection Open
Feature selection is an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of an algorithm, such as a classification algorithm. However, feature selection is a challengi…
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Introduction to Radiomics Open
Radiomics is a rapidly evolving field of research concerned with the extraction of quantitative metrics-the so-called radiomic features-within medical images. Radiomic features capture tissue and lesion characteristics such as heterogeneit…
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Variable selection – A review and recommendations for the practicing statistician Open
Statistical models support medical research by facilitating individualized outcome prognostication conditional on independent variables or by estimating effects of risk factors adjusted for covariates. Theory of statistical models is well‐…
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Machine learning algorithm validation with a limited sample size Open
Advances in neuroimaging, genomic, motion tracking, eye-tracking and many other technology-based data collection methods have led to a torrent of high dimensional datasets, which commonly have a small number of samples because of the intri…
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Benchmarking atlas-level data integration in single-cell genomics Open
Single-cell atlases often include samples that span locations, laboratories and conditions, leading to complex, nested batch effects in data. Thus, joint analysis of atlas datasets requires reliable data integration. To guide integration m…
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Artificial Intelligence in Cardiology Open
Artificial intelligence and machine learning are poised to influence nearly every aspect of the human condition, and cardiology is not an exception to this trend. This paper provides a guide for clinicians on relevant aspects of artificial…
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A Simple New Approach to Variable Selection in Regression, with Application to Genetic Fine Mapping Open
Summary We introduce a simple new approach to variable selection in linear regression, with a particular focus on quantifying uncertainty in which variables should be selected. The approach is based on a new model—the ‘sum of single effect…
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Machine Learning methods for Quantitative Radiomic Biomarkers Open
Radiomics extracts and mines large number of medical imaging features quantifying tumor phenotypic characteristics. Highly accurate and reliable machine-learning approaches can drive the success of radiomic applications in clinical care. I…
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A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data Open
We summarise various ways of performing dimensionality reduction on high-dimensional microarray data. Many different feature selection and feature extraction methods exist and they are being widely used. All these methods aim to remove red…
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Variable selection strategies and its importance in clinical prediction modelling Open
Clinical prediction models are used frequently in clinical practice to identify patients who are at risk of developing an adverse outcome so that preventive measures can be initiated. A prediction model can be developed in a number of ways…
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A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction Open
Due to sharp increases in data dimensions, working on every data mining or machine learning (ML) task requires more efficient techniques to get the desired results. Therefore, in recent years, researchers have proposed and developed many m…
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Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches † Open
Background: With the development of smart grids, accurate electric load forecasting has become increasingly important as it can help power companies in better load scheduling and reduce excessive electricity production. However, developing…
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Feature Selection: A Data Perspective Open
Feature selection, as a data preprocessing strategy, has been proven to be effective and efficient in preparing data (especially high-dimensional data) for various data mining and machine learning problems. The objectives of feature select…
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Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization Open
Accurate assessment of undrained shear strength (USS) for soft sensitive clays is a great concern in geotechnical engineering practice. This study applies novel data-driven extreme gradient boosting (XGBoost) and random forest (RF) ensembl…
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Physical Human Activity Recognition Using Wearable Sensors Open
This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points …
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Machine learning methods for wind turbine condition monitoring: A review Open
This paper reviews the recent literature on machine learning (ML) models that have been used for condition monitoring in wind turbines (e.g. blade fault detection or generator temperature monitoring). We classify these models by typical ML…
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Benchmark for filter methods for feature selection in high-dimensional classification data Open
Feature selection is one of the most fundamental problems in machine learning and has drawn increasing attention due to high-dimensional data sets emerging from different fields like bioinformatics. For feature selection, filter methods pl…
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Evaluation of variable selection methods for random forests and omics data sets Open
Machine learning methods and in particular random forests are promising approaches for prediction based on high dimensional omics data sets. They provide variable importance measures to rank predictors according to their predictive power. …
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<i>iFeature</i>: a Python package and web server for features extraction and selection from protein and peptide sequences Open
Summary Structural and physiochemical descriptors extracted from sequence data have been widely used to represent sequences and predict structural, functional, expression and interaction profiles of proteins and peptides as well as DNAs/RN…
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Cost-Effective Active Learning for Deep Image Classification Open
Recent successes in learning-based image classification, however, heavily\nrely on the large number of annotated training samples, which may require\nconsiderable human efforts. In this paper, we propose a novel active learning\nframework,…
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Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data Open
Deep learning, a state-of-the-art machine learning approach, has shown outstanding performance over traditional machine learning in identifying intricate structures in complex high-dimensional data, especially in the domain of computer vis…
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A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction Open
Machine learning has shown utility in detecting patterns within large, unstructured, and complex datasets. One of the promising applications of machine learning is in precision medicine, where disease risk is predicted using patient geneti…
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Feature selection using Joint Mutual Information Maximisation Open
Feature selection is used in many application areas relevant to expert and intelligent systems, such as data mining and machine learning, image processing, anomaly detection, bioinformatics and natural language processing. Feature selectio…
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Feature dimensionality reduction: a review Open
As basic research, it has also received increasing attention from people that the “curse of dimensionality” will lead to increase the cost of data storage and computing; it also influences the efficiency and accuracy of dealing with proble…
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Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation Open
Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. This study presents a hybrid algorithm that combines similar days (SD) selection, empirical mode decomposition (EMD), and long shor…
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Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm Open
Redundant and irrelevant features in data have caused a long-term problem in network traffic classification. These features not only slow down the process of classification but also prevent a classifier from making accurate decisions, espe…
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A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms Open
Heart disease is one of the most critical human diseases in the world and affects human life very badly. In heart disease, the heart is unable to push the required amount of blood to other parts of the body. Accurate and on time diagnosis …