An Agent-based Software Framework for Machine Learning Tuning

Abstract

Nowadays, the challenge of knowledge discovery is to mine massive amounts of data available online. The most widely used approaches to tackle that challenge are based on machine learning techniques. In spite of being very powerful, those techniques require their parameters to be calibrated in order to generate models with better quality. Such calibration processes are time-consuming and rely on the skills of machine learning experts. Within this context, this research presents a framework based on software agents for automating the calibration of machine learning models. This approach integrates concepts from Agent Oriented Software Engineering (AOSE) and Machine Learning (ML). As a proof of concept, we first train a model for the IRIS dataset and then we show how our approach improves the quality of new models generated by our framework. Keywords. Agent oriented Software Engineering (AOSE); Machine Learning; Time-consuming abstractions with the following properties [6]: (i) autonomyit is the capability of taking their own actions within their environment; (ii) reactivityit is the capability of response to the changes in the environment, which involves a notion of perception of the environment; (iii) social abilityit is the capability of interaction with other agents and possibly humans, and (iv) proactive abilityit is the capability to take actions towards the agent's goals.