A Review of Feature Extraction Optimization in SMS Spam Messages Classification

Zainal, Kamahazira; Jali, Mohd Zalisham

doi:10.1007/978-981-10-2777-2_14

Cited by 12 publications

(6 citation statements)

References 32 publications

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“…Authors Almeida et al [37] [38] argued that the pre-processing has weakened its effect and degrade the classification rate. However, a simulation [28] done in the same field verified that the pre-treatment of a text would amplify the detection rate in distinguishing spam messages. In addition to that, it is also proven that pre-processing has contributed 5% improvement to accuracy value in opinion mining [35].…”

Section: B the Significance Of Text Pre-processingmentioning

confidence: 96%

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The Design and Development of Spam Risk Assessment Prototype: In Silico of Danger Theory Variants

Zainal¹,

Jali²

2017

ijacsa

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Abstract-Now-a-days, data is flowing with various types of information and it is absolutely enormous and moreover, it is in unstructured form. These raw data is meaningless unless it is processed and analyzed to retrieve all the valuable and meaningful information. In this paper, a design and principal functionalities of the system prototype is introduced. A process of information retrieval by applying the text mining with Artificial Immune System (AIS) is proposed to discover the possible level of severity for a Short Messaging Service (SMS) spam. This is expected to be a potential tool in retrieving an implicit danger that a spam might impact to the recipients. Furthermore, the development of this tool can be considered as an emergence of another data mining tool that also exceedingly possible to be embedded with another existing tool.

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Section: B the Significance Of Text Pre-processingmentioning

confidence: 96%

“…There are many research found that applied text mining in SMS spam classification [11] [28]. However, application of text mining for spam messages is not limited to SMS but also include for email, webpage, and social media platform.…”

Section: B the Significance Of Text Pre-processingmentioning

confidence: 99%

The Design and Development of Spam Risk Assessment Prototype: In Silico of Danger Theory Variants

Zainal¹,

Jali²

2017

ijacsa

Self Cite

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“…The research method in this study was based on a contentbased approach. Some of the previous work based on the content are: Jali (2016) carried out an analysis of the ability to control features, analyzing information, and affect circumstances in the classification of SMS spam messages [7]. Kaya and Ertuğrul (2016) have implemented a method based on local ternary patterns to extract two distinct features from SMS messages and many machine learning approaches have been applied to distinguish SMS spam [8].…”

Section: Related Workmentioning

confidence: 99%

SMS Spam Detection Based on Fuzzy Rules and Binary Particle Swarm Optimization

Hameed¹,

Ali²

2021

IJIES

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Over the last decade, the usage of short message services (SMS) as one of the vital communication services on mobile devices has grown. The growth of using this service has correspondingly increased the number of attacks on mobile devices such as SMS Spam. SMS spam is a concern to telecommunications service providers as they annoy the subscribers and cause them to loose commercial. Most current researches have attempted to detect SMS spam using different classifiers. In this paper, we propose a new method that focuses on binary particle swarm optimizationbased fuzzy rules selection for detecting SMS spam messages. First, we extract significant features from the SMS spam dataset. Then, a set of fuzzy rules based on the extracted features is generated. Finally, a binary particle swarm is suggested for picking the more powerful fuzzy rules that reduce the complexity and improve the model's performance. The SMS Spam benchmark dataset was used in the experiment. The attained results of the proposed model provide a recall of 98.8%, precision of 90.8%, F-measure of 94.6% and accuracy of 98.5% that indicate the proposed model can be a promising for detecting SMS spam.

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“…The review proves that the use of support vector machine (SVM) and Naive Bayes leads to efficient performance. Another review for SMS spam classification/filtering is presented in [8]. This review focuses on expanding the number of features used for SMS classification and considers how the number of selected features affects the rate of accuracy.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Security Schema for SMS Spam Message Based on Machine Learning Algorithms

Alshahrani

2021

Int. J. Interact. Mob. Technol.

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<p class="0abstract">SMS spam messages represent one of the most serious threats to current traditional networks. These messages have been particularly prevalent overseas and are harmful to various types of devices. The current filtering scheme employed in conventional systems is unable to expose a large number of messages. To resolve this issue, a new intelligent security system is proposed to reduce the number of spam messages. It can detect novel spam messages that have a direct and negative impact on networks. The proposed system is heavily based on machine learning to explore various types of messages. The primary achievement of our study is the increase in the accuracy ratio as well as the reduction in the number of false alarms. According to the experimental results, it is clear that our system can realize outstanding results, detecting a massive number of massages.</p>

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A Review of Feature Extraction Optimization in SMS Spam Messages Classification

Cited by 12 publications

References 32 publications

The Design and Development of Spam Risk Assessment Prototype: In Silico of Danger Theory Variants

The Design and Development of Spam Risk Assessment Prototype: In Silico of Danger Theory Variants

SMS Spam Detection Based on Fuzzy Rules and Binary Particle Swarm Optimization

Intelligent Security Schema for SMS Spam Message Based on Machine Learning Algorithms

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