Adaptive TreeHive: Ensemble of trees for enhancing imbalanced intrusion classification.

ABSTRACT: Imbalanced intrusion classification is a complex and challenging task as there are few number of instances/intrusions generally considered as minority instances/intrusions in the imbalanced intrusion datasets. Data sampling methods such as over-sampling and under-sampling methods are commonly applied for dealing with imbalanced intrusion data. In over-sampling, synthetic minority instances are generated e.g. SMOTE (Synthetic Minority Over-sampling Technique) and on the contrary, under-sampling methods remove the majority-class instances to create balanced data e.g. random under-sampling. Both over-sampling and under-sampling methods have the disadvantages as over-sampling technique creates overfitting and under-sampling technique ignores a large portion of the data. Ensemble learning in su

SUBMITTER: Sobhani ME 

PROVIDER: S-EPMC12448997 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature