Estimator learning automata for feature subset selection in high‐dimensional spaces, case study: Email spam detection

Seyyedi, Seyyed Hossein; Minaei-Bidgoli, Behrouz

doi:10.1002/dac.3541

Cited by 12 publications

(4 citation statements)

References 57 publications

(63 reference statements)

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“…The theory of LA has found numerous applica tions in the field of computer science. One of the most recent applications of LA include sampling algorithms for stochastic graphs [19], trust propagation in online social networks [22], allocation hub location problem [4], selecting caching nodes in delay tolerant networks [9] and feature subset selection [23] to mention a few. For an updated overview over the theory and applications of LA we refer the reader to the following book [20] and to a recent special issue [21] dedicated to the applications of LA.…”

Section: Stochastic Nfek: State-of-the-artmentioning

confidence: 99%

Two-time scale learning automata: an efficient decision making mechanism for stochastic nonlinear resource allocation

2019

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The Stochastic Non-linear Fractional Equality Knapsack (NFEK) prob lem is a substantial resource allocation problem which admits a large set of applications such as web polling under polling constraints, and constrained estimation. The NFEK problem is usually solved by trial and error based on noisy feedback information from the environment. The available solutions to NFEK are based on the traditional family of Reward-Inaction Learning Au tomata (LA) scheme where the action probabilities are updated based on only the last feedback. Such an update form seems counterproductive for two rea sons: 1) it only uses the last feedback and does not consider the whole history of the feedback and 2) it ignores updates whenever the last feedback does not correspond to a reward. In this pape1� we rather suggest instead a learn ing solution that resorts to the whole history of feedback using the theory of two time-scale separation. Through comprehensive experimental results we show that the proposed solution is not only superior to the state-of-the-art in terms of peak performance but is also robust to the choice of the tuning parameters.

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Section: Stochastic Nfek: State-of-the-artmentioning

confidence: 99%

Two-time scale learning automata: an efficient decision making mechanism for stochastic nonlinear resource allocation

2019

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“…However, the model was theoretical and not tested on real data. Another interesting application was presented in [39]. The authors proposed estimator learning automaton-based subset selection as a new method for feature selection in highdimensional spaces.…”

Section: Related Workmentioning

confidence: 99%

Practical Web Spam Lifelong Machine Learning System with Automatic Adjustment to Current Lifecycle Phase

Luckner

2019

Security and Communication Networks

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Machine learning techniques are a standard approach in spam detection. Their quality depends on the quality of the learning set, and when the set is out of date, the quality of classification falls rapidly. The most popular public web spam dataset that can be used to train a spam detector-WEBSPAM-UK2007-is over ten years old. Therefore, there is a place for a lifelong machine learning system that can replace the detectors based on a static learning set. In this paper, we propose a novel web spam recognition system. The system automatically rebuilds the learning set to avoid classification based on outdated data. Using a built-in automatic selection of the active classifier the system very quickly attains productive accuracy despite a limited learning set. Moreover, the system automatically rebuilds the learning set using external data from spam traps and popular web services. A test on real data from Quora, Reddit, and Stack Overflow proved the high recognition quality. Both the obtained average accuracy and the F-measure were 0.98 and 0.96 for semiautomatic and full-automatic mode, respectively.

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“…Dimensional reduction is achieved with steps such as feature selection and feature extraction. The two steps, feature selection and feature extraction, though both aiming at reducing the number of features, are different in that feature extraction generates new variables while feature selection removes noises without creating new features (Seyyedi & Minaeibidgoli, 2018). The most common methods for feature selection include Term Frequency-Inverse Document Frequency (TF-IDF), Chi-square Statistics, Information Gain, and Mutual Information (Sabbah et al, 2017; Shah & Patel, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…TC techniques are also important methods in knowledge management, such as content-based recommendation (Hawashin et al, 2019; Wijewickrema et al, 2019; Wu et al, 2020), patent classification (Kim et al, 2020), and information extraction (Al-Yahya, 2018). Besides, TC techniques have been frequently applied to the detection of unwanted messages, including short message spam, junk mails, and suspicious malignant mails (Ezpeleta et al, 2017; Hsiao & Chang, 2008; Mujtaba, Shuib, Raj, & Gunalan 2018; Seyyedi & Minaeibidgoli, 2018).…”

Section: Introductionmentioning

confidence: 99%

The Research Trends of Text Classification Studies (2000–2020): A Bibliometric Analysis

Zhu

Lei

2022

Sage Open

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Text Classification (TC) is the process of assigning several different categories to a set of texts. This study aims to evaluate the state of the arts of TC studies. Firstly, TC-related publications indexed in Web of Science were selected as data. In total, 3,121 TC-related publications were published in 760 journals between 2000 and 2020. Then, the bibliographic information was mined to identify the publication trends, important contributors, publication venues, and involved disciplines. Besides, a thematic analysis was performed to extract topics with increasing/decreasing popularity. The findings showed that TC has become a fast-growing interdisciplinary area, and that emerging research powers such as China are playing increasingly important roles in TC research. Moreover, the thematic analysis showed increased interest in topics concerning advanced classification algorithms, performance evaluation methods, and the practical applications of TC. This study will help researchers recognize the recent trends in the area.

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Estimator learning automata for feature subset selection in high‐dimensional spaces, case study: Email spam detection

Cited by 12 publications

References 57 publications

Two-time scale learning automata: an efficient decision making mechanism for stochastic nonlinear resource allocation

Two-time scale learning automata: an efficient decision making mechanism for stochastic nonlinear resource allocation

Practical Web Spam Lifelong Machine Learning System with Automatic Adjustment to Current Lifecycle Phase

The Research Trends of Text Classification Studies (2000–2020): A Bibliometric Analysis

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