Learning k-Nearest Neighbors Classifier from Distributed Data

Most learning algorithms assume that all the relevant data are available on a single computer site. In the emerging networked environments learning tasks are encountering situations in which the relevant data exists in a number of geographically distributed databases that are connected by communication networks. These databases cannot be moved to other network sites due to security, size, privacy, or data-ownership considerations. In this paper we show how a k-nearest classifier algorithm can be adapted for distributed data situations. The objective of our algorithms is to achieve the learning objectives for any data distribution encountered across the network by exchanging local summaries among the participating nodes

Agents for Integrating Distributed Data for Complex Computations

Algorithms for many complex computations assume that all the relevant data is available on a single node of a computer network. In the emerging distributed and networked knowledge environments, databases relevant for computations may reside on a number of nodes connected by a communication network. These data resources cannot be moved to other network sites due to privacy, security, and size considerations. The desired global computation must be decomposed into local computations to match the distribution of data across the network. The capability to decompose computations must be general enough to handle different distributions of data and different participating nodes in each instance of the global computation. In this paper, we present a methodology wherein each distributed data source is represented by an agent. Each such agent has the capability to decompose global computations into local parts, for itself and for agents at other sites. The global computation is then performed by the agent either exchanging some minimal summaries with other agents or travelling to all the sites and performing local tasks that can be done at each local site. The objective is to perform global tasks with a minimum of communication or travel by participating agents across the network

Learning ontology aware classifiers

Many applications of data-driven knowledge discovery processes call for the exploration of data from multiple points of view that reflect different ontological commitments on the part of the learner. Of particular interest in this context are algorithms for learning classifiers from ontologies and data. Against this background, my dissertation research is aimed at the design and analysis of algorithms for construction of robust, compact, accurate and ontology aware classifiers. We have precisely formulated the problem of learning pattern classifiers from attribute value taxonomies (AVT) and partially specified data. We have designed and implemented efficient and theoretically well-founded AVT-based classifier learners. Based on a general strategy of hypothesis refinement to search in a generalized hypothesis space, our AVT-guided learning algorithm adopts a general learning framework that takes into account the tradeoff between the complexity and the accuracy of the predictive models, which enables us to learn a classifier that is both compact and accurate. We have also extended our approach to learning compact and accurate classifier from semantically heterogeneous data sources. We presented a principled way to reduce the problem of learning from semantically heterogeneous data to the problem of learning from distributed partially specified data by reconciling semantic heterogeneity using AVT mappings, and we described a sufficient statistics based solution

Learning predictive models from massive, semantically disparate data

Machine learning approaches offer some of the most successful techniques for constructing predictive models from data. However, applying such techniques in practice requires overcoming several challenges: infeasibility of centralized access to the data because of the massive size of some of the data sets that often exceeds the size of memory available to the learner, distributed nature of data, access restrictions, data fragmentation, semantic disparities between the data sources, and data sources that evolve spatially or temporally (e.g. data streams and genomic data sources in which new data is being submitted continuously). Learning using statistical queries and semantic correspondences that present a unified view of disparate data sources to the learner offer a powerful general framework for addressing some of these challenges. Against this background, this thesis describes (1) approaches to deal with missing values in the statistical query based algorithms for building predictors (Nayve Bayes and decision trees) and the techniques to minimize the number of required queries in such a setting. (2) Sufficient statistics based algorithms for constructing and updating sequence classifiers. (3) Reduction of several aspects of learning from semantically disparate data sources (such as (a) how errors in mappings affect the accuracy of the learned model and (b) how to choose an optimal mapping from among a set of alternative expert-supplied or automatically generated mappings) to the well-studied problems of domain adaptation and learning in presence of noise and (4) a software for learning predictive models from semantically disparate data