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Authors: Camelia Lemnaru ; Andreea Tudose-Vintila ; Andrei Coclici and Rodica Potolea

Affiliation: Technical University of Cluj-Napoca, Romania

ISBN: 978-989-8565-29-7

Keyword(s): Imbalanced Multi-class Classification, Multiple Classifier, Prediction Combination, Anomaly Detection, KDD’99 Data.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Clustering and Classification Methods ; Data Reduction and Quality Assessment ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Mining High-Dimensional Data ; Pre-Processing and Post-Processing for Data Mining ; Symbolic Systems

Abstract: Imbalanced classification problems represent a current challenge for the application of data mining techniques to real-world problems, since learning algorithms are biased towards favoring the majority class(es). The present paper proposes a compound classification architecture for dealing with imbalanced multi-class problems. It comprises of a two-level classification system: a multiple classification model on the first level, which combines the predictions of several binary classifiers, and a supplementary classification model, specialized on identifying “difficult” cases, which is currently under development. Particular attention is allocated to the pre-processing step, with specific data manipulation operations included. Also, a new prediction combination strategy is proposed, which applies a hierarchical decision process in generating the output prediction. We have performed evaluations using an instantiation of the proposed model applied to the field of network intrusion detecti on. The evaluations performed on a dataset derived from the KDD99 data have indicated that our method yields a superior performance for the minority classes to other similar systems from literature, without degrading the overall performance. (More)

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Paper citation in several formats:
Lemnaru, C.; Tudose-Vintila, A.; Tudose-Vintila, A.; Coclici, A.; Coclici, A. and Potolea, R. (2012). A Hybrid Solution for Imbalanced Classification Problems - Case Study on Network Intrusion Detection.In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012) ISBN 978-989-8565-29-7, pages 348-352. DOI: 10.5220/0004142803480352

@conference{kdir12,
author={Camelia Lemnaru. and Andreea Tudose{-}Vintila. and Andreea Tudose{-}Vintila. and Andrei Coclici. and Andrei Coclici. and Rodica Potolea.},
title={A Hybrid Solution for Imbalanced Classification Problems - Case Study on Network Intrusion Detection},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)},
year={2012},
pages={348-352},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004142803480352},
isbn={978-989-8565-29-7},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)
TI - A Hybrid Solution for Imbalanced Classification Problems - Case Study on Network Intrusion Detection
SN - 978-989-8565-29-7
AU - Lemnaru, C.
AU - Tudose-Vintila, A.
AU - Tudose-Vintila, A.
AU - Coclici, A.
AU - Coclici, A.
AU - Potolea, R.
PY - 2012
SP - 348
EP - 352
DO - 10.5220/0004142803480352

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