Feature Selection Techniques and its Importance in Machine Learning: A Survey

N, Thomas. Rincy.; Gupta, Roopam

doi:10.1109/sceecs48394.2020.189

Cited by 11 publications

(5 citation statements)

References 15 publications

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“…The significance of feature selection is multifaceted, reducing computational costs, identifying irrelevant features, and aiding the whole generation of well-classified models [8]. Focusing on the most relevant features also eliminates noise and redundancy, thereby improving the performance and accuracy while also reducing the dimensionality and optimizing the overall ML approaches in diverse fields [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…With the maturation of technology and the concurrent advancement in ML methodologies, a trend toward more intricate and profound models becomes evident. Concomitant with these developments is the emergence of new paradigms in feature selection, as researchers are now endeavoring to incorporate feature selection techniques into deep learning models, underscoring the burgeoning significance of this approach even within the context of contemporary, high-dimensionality, and complex models [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods

Alves,

Mendonça,

Mostafa

et al. 2023

Applied Sciences

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Wind factors significantly influence air travel, and extreme conditions can cause operational disruptions. Machine learning approaches are emerging as a valuable tool for predicting wind patterns. This research, using Madeira International Airport as a case study, delves into the effectiveness of feature creation and selection for wind nowcasting, focusing on predicting wind speed, direction, and gusts. Data from four sensors provided 56 features to forecast wind conditions over intervals of 2, 10, and 20 min. Five feature selection techniques were analyzed, namely mRMR, PCA, RFECV, GA, and XGBoost. The results indicate that combining new wind features with optimized feature selection can boost prediction accuracy and computational efficiency. A strong spatial correlation was observed among sensors at different locations, suggesting that the spatial-temporal context enhances predictions. The best accuracy for wind speed forecasts yielded a mean absolute percentage error of 0.35%, 0.53%, and 0.63% for the three time intervals, respectively. Wind gust errors were 0.24%, 0.33%, and 0.38%, respectively, while wind direction predictions remained challenging with errors above 100% for all intervals.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods

Alves,

Mendonça,

Mostafa

et al. 2023

Applied Sciences

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“…Wrapper techniques select features by exploring various feature subsets and evaluating model performance; this process is independent of the model training. Embedded approaches are integrated with certain machine learning algorithms, including random forest (RF) and support vector machine (SVM), often have inherent evaluation metrics to select optimal features (Thomas and Gupta, 2020). Due to their consideration of training samples and feature interdependencies, embedded approaches typically yield superior model performance compared with other feature selection methods (Kumar, 2014;Thomas and Gupta, 2020).…”

mentioning

confidence: 99%

“…Embedded approaches are integrated with certain machine learning algorithms, including random forest (RF) and support vector machine (SVM), often have inherent evaluation metrics to select optimal features (Thomas and Gupta, 2020). Due to their consideration of training samples and feature interdependencies, embedded approaches typically yield superior model performance compared with other feature selection methods (Kumar, 2014;Thomas and Gupta, 2020). Algorithms frequently used in the modeling stage include partial least squares regression (PLSR) (Knox et al, 2015), ensemble learning (Carranza, 2015;Tan et al, 2020;Lin et al, 2022), SVM (Iglesias, 2020;Chatterjee et al, 2022), artificial neural networks (ANN) (Saikia et al, 2020), and deep learning (Zhang T. et al, 2023).…”

mentioning

confidence: 99%

Estimation of the dolomite content of carbonate rock outcrops based on spectral knowledge and machine learning

Wei,

Shao,

et al. 2024

Front. Earth Sci.

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Accurately estimating the dolomite content in carbonate rocks is crucial for optimizing oil and gas exploration and production strategies. Hyperspectral techniques for estimating dolomite content have advantages in terms of efficiency, cost-effectiveness, and non-destructiveness compared with traditional laboratory methods. Despite the abundance of hyperspectral data, feature selection and extraction remain challenging. In this study, hyperspectral data collected from surface outcrop in the field using the analytical spectral device (ASD) were applied to construct model for estimating dolomite content. Firstly, the data were preprocessed via outlier analysis and continuum transformation. Next, a hybrid approach integrating spectral knowledge with machine learning was proposed and applied to facilitate efficient and precise feature selection of the hyperspectral data; in this approach, preliminary screening based on spectral knowledge is followed by further hyperspectral data feature selection using a random forest algorithm. The selected features were then combined using a support vector regression algorithm to obtain the estimation model. Finally, the accuracy of the model was evaluated using the hyperspectral data from field outcrop samples. To further verify the effectiveness of this method, various combinations of eight input variables and four machine learning algorithms were compared. Among all combinations, our model achieved the highest accuracy with a test R2 value of 0.91 and a root-mean-square error of only 0.122. The proposed method is practical and efficient and provides precise quantitative data for field geologists to identify the mineral distribution in outcrops. Thus, our method provides robust support for understanding reservoir characteristics and has significant practical value in geological surveys and mineral exploration.

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“…However, the viewed literature work used Statistical tools like STATA and Epi Info, and Microsoft excel (Gitige et al, 2021;Mwango et al, 2020), for analyzing the feature importance. This study used machine learning techniques to avoid ignoring of the nonlinear features (Gupta, 2020).…”

Section: Discussionmentioning

confidence: 99%

Machine learning model for prediction and visualization of HIV index testing in northern Tanzania

Chikusi¹

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Infection with the human immunodeficiency virus and acquired immunodeficiency syndrome (HIV/AIDS) continue to pose a threat to Tanzanian society. Various tactics have been used to improve the number of persons who are aware of their HIV status. Index testing stands out among these methods as the most effective way to count the number of HIV contacts who may be at risk of catching HIV from HIV-positive individuals. The current HIV index testing, however, is manual, which presents a number of difficulties, including inaccuracies, is time consuming, and is expensive to operate. In order to forecast and depict HIV index testing, this study presents the findings of the machine-learning model. The software development procedure was in accordance with agile software development principles. The regions of Kilimanjaro, Arusha, and Manyara in Tanzania are where the data was gathered which consisted of 11 features and 6346 samples. The dataset was then separated into training sets with 5075 samples each and testing sets with 1270 samples (80/20). The datasets were subjected to the methods Random Forest (RF), XGBoost, and Artificial Neural Networks (ANN). Random forest MAE (1.1261), XGBoost MAE (1.2340), and ANN MAE (1.1268) were the three results obtained. Random forest algorithms had the lowest mean absolute errors (MAE). Therefore, RF appearing to have the highest performance when compared to the other two algorithms. In comparison to men (17.4%), data visualization reveals that females are more likely to test for HIV and to name their partners (82.6%). Additionally, there were higher instances of persons listing and mentioning their partners in the Kilimanjaro region. This work helped us realize the importance of machine learning in predicting and visualizing HIV index tests in general. The created model can help decision-makers build a viable intervention to stop the spread of HIV and AIDS in our communities. The report suggests that health centers in other areas employ this concept to make their work more straightforward.

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Feature Selection Techniques and its Importance in Machine Learning: A Survey

Cited by 11 publications

References 15 publications

Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods

Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods

Estimation of the dolomite content of carbonate rock outcrops based on spectral knowledge and machine learning

Machine learning model for prediction and visualization of HIV index testing in northern Tanzania

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