Meta-learning in distributed data mining systems: Issues and approaches

Data mining systems aim to discover patterns and extract useful information from facts recorded in databases. A widely adopted approach to this objective is to apply various machine learning algorithms to compute descriptive models of the available data. Here, we explore one of the main challenges in this research area, the development of techniques that scale up to large and possibly physically distributed databases. Meta-learning is a technique that seeks to compute higher-level classifiers (or classification models), called meta-classifiers, that integrate in some principled fashion multiple classifiers computed separately over different databases. This study, describes meta-learning and presents the JAM system (Java Agents for Meta-learning), an agent-based meta-learning system for large-scale data mining applications. Specifically, it identifies and addresses several important desiderata for distributed data mining systems that stem from their additional complexity compared to centralized or host-based systems. Distributed systems may need to deal with heterogenous platforms, with multiple databases and (possibly) different schemas, with the design and implementation of scalable and effective protocols for communicating among the data sites, and the selective and efficient use of the information that is gathered from other peer data sites. Other important problems, intrinsic within ∗Supported in part by an IBM fellowship. data mining systems that must not be ignored, include, first, the ability to take advantage of newly acquired information that was not previously available when models were computed and combine it with existing models, and second, the flexibility to incorporate new machine learning methods and data mining technologies. We explore these issues within the context of JAM and evaluate various proposed solutions through extensive empirical studies.