An Optimized Approach for Spam Message Detection Using C4.5 Classifier with Stochastic Hill Climbing and Genetic Algorithm for Feature Selection
- Title
- An Optimized Approach for Spam Message Detection Using C4.5 Classifier with Stochastic Hill Climbing and Genetic Algorithm for Feature Selection
- Creator
- Saraswathi, D.; Kavitha, R.
- Description
- In the mobile industry, text messaging is a popular feature that is mainly intended to make money for service providers. But spam, which is defined as unsolicited bulk messages that contain commercial content, has become a widespread problem. These spam texts are frequently used to spread phishing links or advertise goods and services in order to make money. The phone alerts the user whenever spam text messages arrive in their inbox. When the user discovers that the message is unsolicited, these unsolicited texts not only take up storage space and waste their time, but they also irritate them. Even with the development of numerous sophisticated algorithms to identify spam, users are still impacted by text message spam. Thus, the mobile sector needs to implement efficient filtering methods. The proposed study uses the C4.5 Decision Tree as the classification model and combines a Genetic Algorithm and Stochastic Hill Climbing to find optimal features in order to detect spam in text messages. This method uses metaheuristic techniques to find the best features, which are then categorized using decision trees. This hybrid model performs better than current classification methods. 2026 IEEE.
- Source
- 2026 2nd International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2026;
- Date
- 01-01-2026
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Classification; Decision Tree; Hybrid approach; Metaheuristic Algorithm; Spam message; Stochastic Hill Climbing
- Coverage
- Saraswathi D., Psg College of Arts & Science, Department of Computer Science, Coimbatore, India; Kavitha R., Christ University, Department of Statistics and Data Science, Bangalore, India
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISBN: 979-833154596-3;
- Format
- online
- Language
- English
- Type
- Conference paper
Collection
Citation
Saraswathi, D.; Kavitha, R., “An Optimized Approach for Spam Message Detection Using C4.5 Classifier with Stochastic Hill Climbing and Genetic Algorithm for Feature Selection,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26049.