Classification is a key factor in accuracy, simplicity, and expressiveness, and it is difficult to optimize all of these factors at the same time. The learning classifier system (LCS) is a suitable technique for addressing an adaptive classification problem. It is a combination of fast approximation and evolutionary optimization techniques. A neural-based learning classifier system (N-LCS) includes an architecture for maintaining expressiveness by incorporating neural networks into a supervised classifier system, which is also an LCS specializing in classification studies. In recent years, studies using deep artificial neural networks have been actively conducted. In particular, deep convolutional neural networks (CNN) provide a powerful representation in an extremely fundamental method and demonstrates the high performance in various domains. In this paper, we exploit various deep CNN architectures in convolutional neural-based learning classifier systems (CN-LCS) combining the CNN and LCS to explore the possibility of a CN-LCS. By using various CNNs as an action of a classifier in an N-LCS, better classification accuracy can be obtained and classifier can be optimized. Experimental results show that our models achieve the higher performance than N-LCS for database intrusion detection as well as two other datasets, and extract effective features from deep representation by projecting data samples learned by several deep CNN models into the feature space.
Bibliographical noteFunding Information:
This work was supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract ( UD160066BD ).
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence