WOLF

Abstract

Applying machine learning techniques to solve real-world problems is a highly iterative process. The process from idea to code and then to experiment may require up to thousands of iterations to find the optimum set of hyper-parameters. Also, it is hard to find best machine learning techniques for a given dataset. The WOLF framework has been designed to simultaneously automate the process of selecting the best algorithm and searching for the optimum hyper-parameters. It can be useful to both who are novice in machine learning and just want to find best algorithm for their dataset, and also to those who are experts in the field and want to compare their new features or algorithm with state of the art techniques. By incorporating the WOLF framework in their designs, it is easier for novices to apply machine learning techniques on their dataset. With a wide range of evaluation metrics provided, WOLF also helps data scientists to develop better intuition towards machine learning techniques and speed up the process of algorithm development. Another main feature of the WOLF framework is that user can easily integrate new algorithms at any stage of the machine learning pipeline. In this paper, we present the WOLF architecture, and demonstrate how it could be used for standard machine learning datasets and for Android malware detection tasks. Experimental results show the flexibility and performance of WOLF.