<scp>HFS‐LightGBM</scp>: A machine learning model based on hybrid feature selection for classifying <scp>ICU</scp> patient readmissions

Qiu, Yan; Ding, Shuai; Yao, Ningguang; Gu, Dawu; Li, Xiaojian

doi:10.1111/exsy.12658

Cited by 5 publications

(3 citation statements)

References 51 publications

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“…Choosing unnecessary features increases the time and effort required to process the data and can even decrease the accuracy of the classifications produced. Filter, wrapper, and embedding approaches in classification models in the literature guarantee accurate labeling 43,44 . In addition, numerous recent productive attempts have made use of optimization strategies 45,46 .…”

Section: Proposed Methodsmentioning

confidence: 99%

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A robust malware traffic classifier to combat security breaches in industry 4.0 applications

Ramaiah

Vanmathi

Ravi

2023

Concurrency and Computation

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SummaryIndustry 4.0 integrates cyber systems, physical devices, and digital networks to automate the industrial process. Many sectors aim to adopt the best practices outlined in Industry 4.0. This indicates well for the future networking of an increasing number of devices. As crucial as intelligent automation is, it is essential that it be protected. The proliferation of Internet‐enabled gadgets could raise vulnerability to a variety of threats, malware among them. Intruders see a synthesis of factors as a chance to carry out their malicious plan. Keeping sensitive data and information protected from malicious software is a high responsibility for all industries. It is critical to have both a trustworthy approach and a large dataset to work with when constructing a malware traffic classifier. Malware's capacity to elude detection by antivirus programs improves with the day. Because this malware has the potential to compromise the entire network, establishing a malware traffic classifier requires a strong approach. As the number of data increases, the classifier has a harder time distinguishing between benign and malicious network entries. As a result, weighing too many factors is a time‐consuming process. To assist with these types of real‐world challenges, we construct an effective hybrid selection component, which is subsequently followed by a neural network classifier in this research. The Malware traffic classifier provided here selects the principal feature using filter and wrapper techniques. The feature columns provided by the feature selection program are used to construct a neural network‐based binary malware classifier. The given malware traffic classification framework was tested using the MTA‐KDD'19 dataset. We set up an experiment in this investigation to examine the way different feature counts perform using a neural‐based classifier. The suggested framework achieves 96.8% accuracy while just considering the bare minimum of five features, which is a substantial increase over alternative methods.

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Section: Proposed Methodsmentioning

confidence: 99%

“…Filter, wrapper, and embedding approaches in classification models in the literature guarantee accurate labeling. 43,44 In addition, numerous recent productive attempts have made use of optimization strategies. 45,46 There are benefits and drawbacks of using various selection procedures for dominant traits.…”

Section: Feature Selectionmentioning

confidence: 99%

A robust malware traffic classifier to combat security breaches in industry 4.0 applications

Ramaiah

Vanmathi

Ravi

2023

Concurrency and Computation

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“…Recall (REC) is the proportion of correctly predicted positive detections to all the observations in actual class as given in Equation ( 8) (Qiu et al, 2021).…”

Section: Experimental Studiesmentioning

confidence: 99%

Multi‐view rank‐based random forest: A new algorithm for prediction in eSports

Birant

2021

Expert Systems

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The main problem associated with the random forest (RF) algorithm is its application of random feature subset selection technique over a single vector. In this technique, the irrelevant or redundant features in a single-view data have equal chances with the important features to be selected for training a classifier, leading to misclassification. To overcome this problem, this article proposes a novel algorithm, called multi-view rank-based random forest (MVRRF). The proposed algorithm builds a set of decision trees from multi-view data by using a rank-based feature selection strategy. The main advantages of our method are that (i) it extends the RF algorithm for multi-view learning, and (ii) it reduces the chances of irrelevant and redundant feature selection, and thus it usually improves the accuracy, generalizability and robustness of the classification models. The aim of our study is to predict the match result of the game League of Legends in electronic sports (eSports). Thus, the eSports teams can define trustworthy strategies through important features. The proposed method can be successfully used in other fields as well as eSports. The experiments that were conducted on a publicly available eSports dataset show that the proposed MVRRF algorithm (93.32%) outperforms the standard RF algorithm (86.38%) on multi-view data in terms of accuracy. Furthermore, the experimental results also show that our method achieved higher performance than the methods tested in the state of the art studies on the same dataset.

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Analysing and Identifying COVID-19 Risk Factors Using Machine Learning Algorithm with Smartphone Application

Siddiqui

Hossain²,

Asaduzzaman³

et al. 2022

Inventive Systems and Control

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HFS‐LightGBM: A machine learning model based on hybrid feature selection for classifying ICU patient readmissions

Cited by 5 publications

References 51 publications

A robust malware traffic classifier to combat security breaches in industry 4.0 applications

A robust malware traffic classifier to combat security breaches in industry 4.0 applications

Multi‐view rank‐based random forest: A new algorithm for prediction in eSports

Analysing and Identifying COVID-19 Risk Factors Using Machine Learning Algorithm with Smartphone Application

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