A Survey on Feature Selection

Miao, Jianyu; Niu, Lingfeng

doi:10.1016/j.procs.2016.07.111

Cited by 415 publications

(231 citation statements)

References 22 publications

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“…With the surge of available data for machine learning applications, there has been renewed interest in DRA as a means to reduce the scale of the input data to a manageable size [29]. As depicted in Fig.…”

Section: Dra Feature Selectionmentioning

confidence: 99%

“…As depicted in Fig. 3, the feature selection aspect of DRA may be categorized as using label information (supervised, semi-supervised, and unsupervised) and selection strategies (filter, wrapper, and embedded) [29,30,31].…”

Section: Dra Feature Selectionmentioning

confidence: 99%

“…The methods here use supervised approaches, i.e., labeled data, whereas semi-supervised and unsupervised approaches use partially-labeled or unlabeled data, respectively [29]. The DRA methods here include two selection strategies that include filter [29] and [30]. Shading indicates areas covered here.…”

Section: Dra Feature Selectionmentioning

confidence: 99%

“…Wrapper methods differ in that they "use the intended learning algorithm itself to evaluate the features" [29] and are optimized for that learning algorithm [30]. While this "optimization" could be a strength, wrapper methods are limited since they are only intended to work with that same learning algorithm, and therefore may suffer from overfitting [31].…”

Section: Dra Feature Selectionmentioning

confidence: 99%

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DNA Feature Selection for Discriminating WirelessHART IIoT Devices

Rondeau

Temple

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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The proliferation of Wireless Highway Addressable Remote Transducer (WirelessHART) communications in support of Industrial Internet of Things (IIoT) applications is accompanied by increased vulnerability concerns that amplify the need for improved pre-attack security and post-attack forensic methods. This paper summarizes demonstration activity aimed at applying Time Domain Distinct Native Attribute (TD-DNA) fingerprinting and improving feature selection to increase computational efficiency and the potential for near-real time operational application. Assessments include both pre-classification and post-classification dimensional reduction using TD-DNA fingerprint features extracted from experimentally collected WirelessHART signals.Results show that pre-classification selection methods are superior, with average percent correct classification differential of 8% < %CD < 1% being maintained using selected feature subsets containing only 24 (10%) of the 243 full-dimensional features.

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Section: Dra Feature Selectionmentioning

confidence: 99%

Section: Dra Feature Selectionmentioning

confidence: 99%

Section: Dra Feature Selectionmentioning

confidence: 99%

Section: Dra Feature Selectionmentioning

confidence: 99%

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DNA Feature Selection for Discriminating WirelessHART IIoT Devices

Rondeau

Temple

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…In fact, discretization can be useful when creating probability mass/density functions and also many machine learning methods produce better results when discretizing continuous attributes ( Kotsiantis & Kanellopoulos, 2005 ). On the other hand, features selection methods produce simplified models that have shorter training and operational time and also more general in order to reduce the problem of overfitting ( Miao & Niu, 2016 ). For the third dimension, we can experiment other clustering algorithms like agglomerative clustering which is widely used in information retrieval.…”

Section: Resultsmentioning

confidence: 99%

Unsupervised collective-based framework for dynamic retraining of supervised real-time spam tweets detection model

Washha

Qaroush

Mezghani

et al. 2019

Expert Systems with Applications

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Spam Social spammersTwitter stream a b s t r a c t Twitter is one of the most popular social platforms. It has changed the way of communication and in-formation dissemination through its real-time messaging mechanism. Recently, it has been used by re-searchers and industries as a new source of data for various intelligent systems, such as tweet sentiment analysis and recommendation systems, which require high data quality. However, due to its flexibility and popularity, Twitter has become the main target for spamming activities such as phishing legitimate users or spreading malicious software, which introduces new security issues and waste resources. There-fore, researchers have developed various machine-learning algorithms to reveal Twitter spam. However, as spammers have become smarter and more crafty, the characteristics of the spam tweets are varying over time making these methods inefficient to detect new spammers tricks and strategies. In addition, some of the employed methods (e.g. blacklisting) or spammer features (e.g. graph-based features) are extremely time-consuming, which hinders the ability to detect spammer activities in real-time. In this paper, we introduce a framework to deal with the volatility of the spam contents and new spamming patterns, called the spam drift. The framework combines the strength of unsupervised machine learning approach, which learns from unlabeled tweets, to retrain a real-time supervised tweet-level spam detec-tion model in a batch mode. A set of experiments on a largescale data set show the effectiveness of the proposed online unsupervised method in adaptively discovers and learns the patterns of new spam activities and achieve stable recall values reaching more than 95%. Although the average spam precision of our method is around 60%, the high spam recall values show the ability of our proposed method in reducing spam drift problems compared to traditional machine learning algorithms.

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Feature Selection

2019

Condition Monitoring With Vibration Signals

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A Survey on Feature Selection

Cited by 415 publications

References 22 publications

DNA Feature Selection for Discriminating WirelessHART IIoT Devices

DNA Feature Selection for Discriminating WirelessHART IIoT Devices

Unsupervised collective-based framework for dynamic retraining of supervised real-time spam tweets detection model

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