Instance-Based Learning Algorithms
TLDR
This paper describes how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy and extends the nearest neighbor algorithm, which has large storage requirements.Abstract:
Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances to solve incremental learning tasks. In this paper, we describe a framework and methodology, called instance-based learning, that generates classification predictions using only specific instances. Instance-based learning algorithms do not maintain a set of abstractions derived from specific instances. This approach extends the nearest neighbor algorithm, which has large storage requirements. We describe how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy. While the storage-reducing algorithm performs well on several real-world databases, its performance degrades rapidly with the level of attribute noise in training instances. Therefore, we extended it with a significance test to distinguish noisy instances. This extended algorithm's performance degrades gracefully with increasing noise levels and compares favorably with a noise-tolerant decision tree algorithm.read more
Citations
More filters
Journal ArticleDOI
Structure–semantics interplay in complex networks and its effects on the predictability of similarity in texts
TL;DR: This study introduces ways to classify texts employing concepts of complex networks, which may be able to capture syntactic, semantic and even pragmatic features, and shows that topological features of the networks representing texts can enhance the ability to identify machine translation systems in particular cases.
Journal ArticleDOI
Local lazy regression: making use of the neighborhood to improve QSAR predictions.
TL;DR: This work investigates the use of local lazy regression (LLR), which obtains a prediction for a query molecule using its local neighborhood, rather than considering the whole data set, which is especially useful for very large data sets because no a priori model need be built.
Journal ArticleDOI
An overview of regression techniques for knowledge discovery
Ilhan Uysal,H. Altay Güvenir +1 more
TL;DR: This paper reviews the important techniques and algorithms for regression developed by both machine learning and statistics and includes Locally Weighted Regression, rule-based regression, Projection Pursuit Regression and recursive partitioning regression methods that induce regression trees.
Proceedings ArticleDOI
Optimizing MapReduce for GPUs with effective shared memory usage
Linchuan Chen,Gagan Agrawal +1 more
TL;DR: A new implementation of MapReduce for GPUs is proposed, which is very effective in utilizing shared memory, a small programmable cache on modern GPUs.
Journal ArticleDOI
Cluster-based instance selection for machine classification
TL;DR: The paper proposes a cluster-based instance selection approach with the learning process executed by the team of agents and discusses its four variants and investigates the influence of the clustering method used on the quality of the classification.
References
More filters
Journal ArticleDOI
Classification and Regression Trees.
Journal ArticleDOI
Induction of Decision Trees
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.
MonographDOI
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
Book
Classification and regression trees
TL;DR: The methodology used to construct tree structured rules is the focus of a monograph as mentioned in this paper, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
Journal ArticleDOI
Nearest neighbor pattern classification
Thomas M. Cover,Peter E. Hart +1 more
TL;DR: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.