# Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text

arXiv: https://arxiv.org/abs/2110.15718 · HTML: https://mlsanity.com/paper/2110.15718/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 43 references that are in the map.
- First posted on arXiv 10 Oct 2021.

## Builds on (most cited first, 2 of 2)

- [Deep Forest](https://mlsanity.com/paper/1702.08835/) (2017)
- [Multi-split Optimized Bagging Ensemble Model Selection for Multi-class Educational Data Mining](https://mlsanity.com/paper/2006.05031/) (2020)

## Built on by (most cited first, 1 of 1)

- [OptBA: Optimizing Hyperparameters with the Bees Algorithm for Improved Medical Text Classification](https://mlsanity.com/paper/2303.08021/) (2023)

## Abstract (arXiv)

The increase in people's use of mobile messaging services has led to the spread of social engineering attacks like phishing, considering that spam text is one of the main factors in the dissemination of phishing attacks to steal sensitive data such as credit cards and passwords. In addition, rumors and incorrect medical information regarding the COVID-19 pandemic are widely shared on social media leading to people's fear and confusion. Thus, filtering spam content is vital to reduce risks and threats. Previous studies relied on machine learning and deep learning approaches for spam classification, but these approaches have two limitations. Machine learning models require manual feature engineering, whereas deep neural networks require a high computational cost. This paper introduces a dynamic deep ensemble model for spam detection that adjusts its complexity and extracts features automatically. The proposed model utilizes convolutional and pooling layers for feature extraction along with base classifiers such as random forests and extremely randomized trees for classifying texts into spam or legitimate ones. Moreover, the model employs ensemble learning procedures like boosting and bagging. As a result, the model achieved high precision, recall, f1-score and accuracy of 98.38%.