Incremental Learning

Incremental Learning

Abdelhamid Bouchachia (University of Klagenfurt, Austria)
Copyright: © 2009 |Pages: 7
DOI: 10.4018/978-1-60566-010-3.ch156
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

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.
Chapter Preview
Top

Background

IL is a key issue in applications where data arrives over long periods of time and/or where storage capacities are very limited. Most of the knowledge learning literature reports on learning models that are one-shot experience. Once the learning stage is exhausted, the induced knowledge is no more updated. Thus, the performance of the system depends heavily on the data used during the learning (knowledge extraction) phase. Shifts of trends in the arriving data cannot be accounted for.

Algorithms with an IL ability are of increasing importance in many innovative applications, e.g., video streams, stock market indexes, intelligent agents, user profile learning, etc. Hence, there is a need to devise learning mechanisms that are able of accommodating new data in an incremental way, while keeping the system under use. Such a problem has been studied in the framework of adaptive resonance theory (Carpenter et al., 1991). This theory has been proposed to efficiently deal with the stability-plasticity dilemma. Formally, a learning algorithm is totally stable if it keeps the acquired knowledge in memory without any catastrophic forgetting. However, it is not required to accommodate new knowledge. On the contrary, a learning algorithm is completely plastic if it is able to continually learn new knowledge without any requirement on preserving the knowledge previously learned. The dilemma aims at accommodating new data (plasticity) without forgetting (stability) by generating knowledge elements over time whenever the new data conveys new knowledge elements worth considering.

Basically there are two schemes to accommodate new data. To retrain the algorithm from scratch using both old and new data is known as revolutionary strategy. In contrast, an evolutionary continues to train the algorithm using only the new data (Michalski, 1985). The first scheme fulfills only the stability requirement, whereas the second is a typical IL scheme that is able to fulfill both stability and plasticity. The goal is to make a tradeoff between the stability and plasticity ends of the learning spectrum as shown in Figure 1.

Figure 1.

Learning spectrum

As noted in (Polikar et al., 2000), there are many approaches referring to some aspects of IL. They exist under different names like on-line learning, constructive learning, lifelong learning, and evolutionary learning. Therefore, a definition of IL turns out to be vital:

Complete Chapter List

Search this Book:
Reset