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Authors: J. M. Martínez-Otzeta and B. Sierra

Affiliation: University of the Basque Country, Spain

Keyword(s): Supervised Classification, Nearest Neighbor, k-NN, Machine Learning, Pattern Recognition.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Business Analytics ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Datamining ; Enterprise Information Systems ; Health Information Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Verification and Validation of Knowledge-Based Systems

Abstract: The k-Nearest Neighbor (k-NN) classification method assigns to an unclassified point the class of the nearest of a set of previously classified points. A problem that arises when aplying this technique is that each labeled sample is given equal importance in deciding the class membership of the pattern to be classified, regardless of the typicalness of each neighbor. We report on the application of a new hybrid version named Iterated Probabilistic Weighted k Nearest Neighbor algorithm (IPW-k-NN) which classifies new cases based on the probability distribution each case has to belong to each class. These probabilities are computed for each case in the training database according to the k Nearest Neighbors it has in this database; this is a new way to measure the typicalness of a given case with regard to every class. Experiments have been carried out using UCI Machine Learning Repository well-known databases and performing 10-fold cross-validation to validate the results obtained in each of them. Three different distances (Euclidean, Camberra and Chebychev) are used in the comparison done. (More)

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Paper citation in several formats:
M. Martínez-Otzeta, J. and Sierra, B. (2004). ANALYSIS OF THE ITERATED PROBABILISTIC WEIGHTED K NEAREST NEIGHBOR METHOD, A NEW DISTANCE-BASED ALGORITHM. In Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 972-8865-00-7; ISSN 2184-4992, SciTePress, pages 233-240. DOI: 10.5220/0002605402330240

@conference{iceis04,
author={J. {M. Martínez{-}Otzeta}. and B. Sierra.},
title={ANALYSIS OF THE ITERATED PROBABILISTIC WEIGHTED K NEAREST NEIGHBOR METHOD, A NEW DISTANCE-BASED ALGORITHM},
booktitle={Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2004},
pages={233-240},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002605402330240},
isbn={972-8865-00-7},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - ANALYSIS OF THE ITERATED PROBABILISTIC WEIGHTED K NEAREST NEIGHBOR METHOD, A NEW DISTANCE-BASED ALGORITHM
SN - 972-8865-00-7
IS - 2184-4992
AU - M. Martínez-Otzeta, J.
AU - Sierra, B.
PY - 2004
SP - 233
EP - 240
DO - 10.5220/0002605402330240
PB - SciTePress

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