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LICIC: Less Important Components for Imbalanced Multiclass Classification

Georgia Institute of Technology, Atlanta, GA 30332, USA
Dipartimento di Informatica, Università degli studi di Bari, 70121 Bari, Italy
Author to whom correspondence should be addressed.
Information 2018, 9(12), 317;
Received: 22 October 2018 / Revised: 19 November 2018 / Accepted: 6 December 2018 / Published: 9 December 2018
(This article belongs to the Special Issue eHealth and Artificial Intelligence)
PDF [3647 KB, uploaded 9 December 2018]


Multiclass classification in cancer diagnostics, using DNA or Gene Expression Signatures, but also classification of bacteria species fingerprints in MALDI-TOF mass spectrometry data, is challenging because of imbalanced data and the high number of dimensions with respect to the number of instances. In this study, a new oversampling technique called LICIC will be presented as a valuable instrument in countering both class imbalance, and the famous “curse of dimensionality” problem. The method enables preservation of non-linearities within the dataset, while creating new instances without adding noise. The method will be compared with other oversampling methods, such as Random Oversampling, SMOTE, Borderline-SMOTE, and ADASYN. F1 scores show the validity of this new technique when used with imbalanced, multiclass, and high-dimensional datasets. View Full-Text
Keywords: imbalanced learn; smote; SVM; KPCA; kernel; class imbalance imbalanced learn; smote; SVM; KPCA; kernel; class imbalance

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Dentamaro, V.; Impedovo, D.; Pirlo, G. LICIC: Less Important Components for Imbalanced Multiclass Classification. Information 2018, 9, 317.

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