A survey on feature selection methods for mixed data

Solorio-Fernández, Saúl; Carrasco-Ochoa, Jesús Ariel; Martínez-Trinidad, José Fco.

doi:10.1007/s10462-021-10072-6

Cited by 29 publications

(4 citation statements)

References 101 publications

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“…Wrapper methods are computationally demanding as they involve evaluating different subsets of features by repeatedly training and validating the model [ 36 ]. These methods can accurately select the most relevant features but may not be suitable for resource-constrained devices.…”

Section: Methods For Glucose Level Predictionmentioning

confidence: 99%

Constrained IoT-Based Machine Learning for Accurate Glycemia Forecasting in Type 1 Diabetes Patients

Rodríguez‐Rodríguez

Campo-Valera

Rodríguez

et al. 2023

Sensors

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Individuals with diabetes mellitus type 1 (DM1) tend to check their blood sugar levels multiple times daily and utilize this information to predict their future glycemic levels. Based on these predictions, patients decide on the best approach to regulate their glucose levels with considerations such as insulin dosage and other related factors. Nevertheless, modern developments in Internet of Things (IoT) technology and innovative biomedical sensors have enabled the constant gathering of glucose level data using continuous glucose monitoring (CGM) in addition to other biomedical signals. With the use of machine learning (ML) algorithms, glycemic level patterns can be modeled, enabling accurate forecasting of this variable. Constrained devices have limited computational power, making it challenging to run complex machine learning algorithms directly on these devices. However, by leveraging edge computing, using lightweight machine learning algorithms, and performing preprocessing and feature extraction, it is possible to run machine learning algorithms on constrained devices despite these limitations. In this paper we test the burdens of some constrained IoT devices, probing that it is feasible to locally predict glycemia using a smartphone, up to 45 min in advance and with acceptable accuracy using random forest.

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Section: Methods For Glucose Level Predictionmentioning

confidence: 99%

Constrained IoT-Based Machine Learning for Accurate Glycemia Forecasting in Type 1 Diabetes Patients

Rodríguez‐Rodríguez

Campo-Valera

Rodríguez

et al. 2023

Sensors

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“…The Frobenius norm ‖X − X � ‖ 2 F 1 Introduction Dimension reduction is crucial in machine learning for simplifying complex data sets (Van Der Maaten et al 2009), reducing computational complexity (Ray et al 2021), and mitigating the curse of dimensionality (Talpur et al 2023), ultimately improving model performance and interpretability. Dimension reduction encompasses two primary approaches: feature selection (Solorio-Fernández et al 2022), which involves choosing a subset of the most informative features from the original data-set to reduce dimensionality while maintaining interpretability; and feature extraction , a method where new, lower-dimensional features are derived from the original data to capture essential patterns and relationships.…”

Section: List Of Symbols Xmentioning

confidence: 99%

Autoencoders and their applications in machine learning: a survey

Berahmand,

Daneshfar,

Salehi

et al. 2024

Artif Intell Rev

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Autoencoders have become a hot researched topic in unsupervised learning due to their ability to learn data features and act as a dimensionality reduction method. With rapid evolution of autoencoder methods, there has yet to be a complete study that provides a full autoencoders roadmap for both stimulating technical improvements and orienting research newbies to autoencoders. In this paper, we present a comprehensive survey of autoencoders, starting with an explanation of the principle of conventional autoencoder and their primary development process. We then provide a taxonomy of autoencoders based on their structures and principles and thoroughly analyze and discuss the related models. Furthermore, we review the applications of autoencoders in various fields, including machine vision, natural language processing, complex network, recommender system, speech process, anomaly detection, and others. Lastly, we summarize the limitations of current autoencoder algorithms and discuss the future directions of the field.

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“…Feature selection methods for mixed data 42 Solorio-Fernandez et al conducted a survey on feature selection methods for mixed data. While informative, this survey does not propose novel techniques and lacks specific insights into addressing feature selection challenges in high-dimensional data.…”

Section: In-depth Review Of Existing Machine Learning Models Used For...mentioning

confidence: 99%

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

Potharlanka,

2024

Sci Rep

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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 in terms of robustness, computational load, or capability to capture complex interactions among features. Despite the utility of metaheuristic algorithms like Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and Whale Optimization (WOA) in feature selection, there still exists a gap in efficiently incorporating feature importance feedback into these processes. This paper presents a novel approach that integrates the strengths of PSO, FA, and WOA algorithms into an ensemble model and further enhances its performance by incorporating a Deep Q-Learning framework for relevance feedbacks. The Deep Q-Learning module intelligently updates feature importance based on model performance, thereby fine-tuning the selection process iteratively. Our ensemble model demonstrates substantial gains in effectiveness over traditional and individual metaheuristic approaches. Specifically, the proposed model achieved a 9.5% higher precision, an 8.5% higher accuracy, an 8.3% higher recall, a 4.9% higher AUC, and a 5.9% higher specificity across multiple software bug prediction datasets and samples. By resolving some of the key issues in existing feature selection methods and achieving superior performance metrics, this work paves the way for more robust and efficient machine learning models in various applications, from healthcare to natural language processing scenarios. This research provides an innovative framework for feature selection that promises not only superior performance but also offers a flexible architecture that can be adapted for a variety of machine learning challenges.

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A survey on feature selection methods for mixed data

Cited by 29 publications

References 101 publications

Constrained IoT-Based Machine Learning for Accurate Glycemia Forecasting in Type 1 Diabetes Patients

Constrained IoT-Based Machine Learning for Accurate Glycemia Forecasting in Type 1 Diabetes Patients

Autoencoders and their applications in machine learning: a survey

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

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