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
In vivo, in vitro and in silico methods for small molecule transfer across the BBB
TL;DR: This review gives a comprehensive overview of different approaches currently considered in drug discovery to circumvent the lack of small molecule transfer through the BBB, together with their inherent advantages and disadvantages.
Journal ArticleDOI
Speeding up incremental wrapper feature subset selection with Naive Bayes classifier
TL;DR: This paper studies how under certain circumstances the wrapper FSS process can be speeded up by embedding the classifier into the wrapper algorithm, instead of dealing with it as a black-box.
Journal ArticleDOI
The use of unmanned aerial vehicles and wireless sensor networks for spraying pesticides
Bruno S. Faiçal,Fausto Guzzo da Costa,Gustavo Pessin,Jó Ueyama,Heitor Freitas,Alexandre Colombo,Pedro H. Fini,Leandro A. Villas,Fernando Santos Osório,Patricia A. Vargas,Torsten Braun +10 more
TL;DR: The results show that the use of the feedback information from the sensors to make adjustments to the routes could significantly reduce the waste of pesticides and fertilizers.
Journal ArticleDOI
Unsupervised stratification of cross-validation for accuracy estimation
TL;DR: New accuracy estimation methods which are extensions of the k-fold cross-validation method are proposed, attempting to construct more representative folds, therefore reducing the bias of the resulting estimator.
Proceedings Article
Exploiting synergy between ontologies and recommender systems
TL;DR: This paper investigates the synergy between a web-based research paper recommender system and an ontology containing information automatically extracted from departmental databases available on the web, and the ontology's interest-acquisition problem.
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.