Using learning automata to determine proper subset size in high-dimensional spaces

Seyyedi, Seyyed Hossein; Minaei-Bidgoli, Behrouz

doi:10.1080/0952813x.2016.1186229

Cited by 3 publications

(2 citation statements)

References 38 publications

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“…All the experiments consisted of 2 phases. The first was an operation called FSAP (finding the best subset upon actual performance), which is for identifying the actual classifier performance. This step was done to have a baseline to evaluate the results of ESS.…”

Section: Resultsmentioning

confidence: 99%

“…Such a goal is pursued in Seyyedi and Minaei‐Bidgoli, which introduced a method called Finding the best candidate Subset using Learning Automata (FSLA) and applied it for dimension reduction in text classification. FSLA combines wrapper and filter ideas and uses a linear learning automaton (LA) to select an appropriate subset of features.…”

Section: Introductionmentioning

confidence: 99%

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

Seyyedi

Minaei-Bidgoli

2018

Int J Communication

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One of the difficult challenges facing data miners is that algorithm performance degrades if the feature space contains redundant or irrelevant features. Therefore, as a critical preprocess task, dimension reduction is used to build a smaller space containing valuable features. There are 2 different approaches for dimension reduction: feature extraction and feature selection, which itself is divided into wrapper and filter approaches. In high-dimensional spaces, feature extrac-

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Seyyedi

Minaei-Bidgoli

2018

Int J Communication

Self Cite

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Achieving Fair Load Balancing by Invoking a Learning Automata-Based Two-Time-Scale Separation Paradigm

Yazidi

Hassan

Hammer

et al. 2021

IEEE Trans. Neural Netw. Learning Syst.

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Enhancing Effectiveness of Dimension Reduction in Text Classification

Seyyedi

Minaei-Bidgoli

2016

Int. J. Artif. Intell. Tools

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Nowadays, text is one prevalent forms of data and text classification is a widely used data mining task, which has various application fields. One mass-produced instance of text is email. As a communication medium, despite having a lot of advantages, email suffers from a serious problem. The number of spam emails has steadily increased in the recent years, leading to considerable irritation. Therefore, spam detection has emerged as a separate field of text classification. A primary challenge of text classification, which is more severe in spam detection and impedes the process, is high-dimensionality of feature space. Various dimension reduction methods have been proposed that produce a lower dimensional space compared to the original. These methods are divided mainly into two groups: feature selection and feature extraction. This research deals with dimension reduction in the text classification task and especially performs experiments in the spam detection field. We employ Information Gain (IG) and Chi-square Statistic (CHI) as well-known feature selection methods. Also, we propose a new feature extraction method called Sprinkled Semantic Feature Space (SSFS). Furthermore, this paper presents a new hybrid method called IG_SSFS. In IG_SSFS, we combine the selection and extraction processes to reap the benefits from both. To evaluate the mentioned methods in the spam detection field, experiments are conducted on some well-known email datasets. According to the results, SSFS demonstrated superior effectiveness over the basic selection methods in terms of improving classifiers’ performance, and IG_SSFS further enhanced the performance despite consuming less processing time.

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Using learning automata to determine proper subset size in high-dimensional spaces

Cited by 3 publications

References 38 publications

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

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

Achieving Fair Load Balancing by Invoking a Learning Automata-Based Two-Time-Scale Separation Paradigm

Enhancing Effectiveness of Dimension Reduction in Text Classification

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