论文标题
使用遗传编程的不断发展的角色级底座体系结构
Evolving Character-Level DenseNet Architectures using Genetic Programming
论文作者
论文摘要
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，但是对使用字符级densenet（char-densenet）体系结构进行文本分类任务进行了有限的研究。目前尚不清楚哪些Densenet架构对于文本分类任务是最佳的。设计，培训和测试char-densenets的迭代任务是需要专家领域知识的NP困难问题。进化深度学习（EDL）已用于自动为图像分类域设计CNN体系结构，从而减轻对专家领域知识的需求。这项研究演示了有关使用EDL进化用于文本分类任务的Char-Densenet架构的第一项工作。一种新型的基于遗传编程的算法（GP致密），再加上间接编码方案，促进了性能的CHAR DENSENET架构的演变。该算法在两个流行的文本数据集上进行评估，并且最佳进化模型对四个当前最新角色级别CNN和Densenet模型进行了基准测试。结果表明，该算法在两个数据集中都会以模型精度和三个最先进的模型在参数大小方面发展了两个数据集的性能模型。
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.
