Data mining and knowledge discovery is about creating a comprehensible model of the data. Such a model may take different forms going from simple association rules to complex reasoning system. One of the fundamental aspects this model has to fulfill is adaptivity. This aspect aims at making the process of knowledge extraction continually maintainable and subject to future update as new data become available. We refer to this process as knowledge learning. Knowledge learning systems are traditionally built from data samples in an off-line one-shot experiment. Once the learning phase is exhausted, the learning system is no longer capable of learning further knowledge from new data nor is it able to update itself in the future. In this chapter, we consider the problem of incremental learning (IL). We show how, in contrast to off-line or batch learning, IL learns knowledge, be it symbolic (e.g., rules) or sub-symbolic (e.g., numerical values) from data that evolves over time. The basic idea motivating IL is that as new data points arrive, new knowledge elements may be created and existing ones may be modified allowing the knowledge base (respectively, the system) to evolve over time. Thus, the acquired knowledge becomes self-corrective in light of new evidence. This update is of paramount importance to ensure the adaptivity of the system. However, it should be meaningful (by capturing only interesting events brought by the arriving data) and sensitive (by safely ignoring unimportant events). Perceptually, IL is a fundamental problem of cognitive development. Indeed, the perceiver usually learns how to make sense of its sensory inputs in an incremental manner via a filtering procedure. In this chapter, we will outline the background of IL from different perspectives: machine learning and data mining before highlighting our IL research, the challenges, and the future trends of IL.
Incremental and developmental perspectives for general-purpose learning systems
