Evolving Character-Level DenseNet Architectures using Genetic Programming

Evolving Character-Level DenseNet Architectures using Genetic Programming

DenseNet architectures have demonstrated impressive performance in image classification tasks, but limited research has been conducted on using character-level DenseNet (char-DenseNet) architectures for text classification tasks. It is not clear what DenseNet architectures are optimal for text classification tasks.The iterative task of designing, training and testing of char-DenseNets is an NP-Hard problem that requires expert domain knowledge. Evolutionary deep learning (EDL) has been used to automatically design CNN architectures for the image classification domain, thereby mitigating the need for expert domain knowledge. This study demonstrates the first work on using EDL to evolve char-DenseNet architectures for text classification tasks. A novel genetic programming-based algorithm (GP-Dense) coupled with an indirect-encoding scheme, facilitates the evolution of performant char DenseNet architectures. The algorithm is evaluated on two popular text datasets, and the best-evolved models are benchmarked against four current state-of-the-art character-level CNN and DenseNet models. Results indicate that the algorithm evolves performant models for both datasets that outperform two of the state-of-the-art models in terms of model accuracy and three of the state-of-the-art models in terms of parameter size.

使用遗传编程发展字符级DenseNet架构

DenseNet体系结构在图像分类任务中表现出了令人印象深刻的性能，但是对于将字符级DenseNet（char-DenseNet）体系结构用于文本分类任务的研究很少。尚不清楚哪种DenseNet体系结构最适合文本分类任务。.. 设计，训练和测试char-DenseNets的迭代任务是NP-Hard问题，需要专家级领域知识。进化深度学习（EDL）已用于为图像分类领域自动设计CNN架构，从而减轻了对专家领域知识的需求。这项研究展示了使用EDL为文本分类任务发展char-DenseNet体系结构的第一项工作。一种新颖的基于遗传编程的算法（GP-Dense）与间接编码方案相结合，促进了高性能char DenseNet体系结构的发展。该算法在两个流行的文本数据集上进行了评估，并且根据四个当前最新的字符级CNN和DenseNet模型对发展最快的模型进行了基准测试。 （阅读更多）