Resampling imbalanced data for network intrusion detection datasets
Journal of Big Data, 2021 Machine learning plays an increasingly significant role in the building of Network Intrusion Detection Systems. However, machine learning models trained with imbalanced cybersecurity data cannot recognize minority data, hence attacks, effectively.
Sikha Bagui, Kunqi Li
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Multi-class protein fold classification using a new ensemble machine learning approach. [PDF]
, 2003 Protein structure classification represents an important process in understanding the associations between sequence and structure as well as possible functional and evolutionary relationships.
Deville, Y, Gilbert, D, Tan, A
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Probability density function estimation based over-sampling for imbalanced two-class problems [PDF]
, 2012 A novel probability density function (PDF) estimation based over-sampling approach is proposed for two-class imbalanced classification problems. The Parzen-window kernel function is applied to estimate the PDF of the positive class, from which synthetic ...
Hong, Xia +3 more
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On combination of SMOTE and particle swarm optimization based radial basis function classifier for imbalanced problems [PDF]
, 2011 The combination of the synthetic minority oversampling technique (SMOTE) and the radial basis function (RBF) classifier is proposed to deal with classification for imbalanced two-class data.
Hong, Xia +3 more
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A critical assessment of imbalanced class distribution problem: the case of predicting freshmen student attrition [PDF]
, 2013 Predicting student attrition is an intriguing yet challenging problem for any academic institution. Class-imbalanced data is a common in the field of student retention, mainly because a lot of students register but fewer students drop out. Classification
Thammasiri, Dech +3 more
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Enhancing classification performance of multi-class imbalanced data using the OAA-DB algorithm [PDF]
, 2012 In data classification, the problem of imbalanced class distribution has attracted many attentions. Most efforts have used to investigate the problem mainly for binary classification.
Wong, K.W. +3 more
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ANN vs. SVM : which one performs better in classification of MCCs in mammogram imaging [PDF]
, 2012 Classification of microcalcification clusters from mammograms plays essential roles in computer-aided diagnosis for early detection of breast cancer, where support vector machine (SVM) and artificial neural network (ANN) are two commonly used techniques.
Ren, Jinchang
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A kernel-based two-class classifier for imbalanced data sets [PDF]
, 2007 Many kernel classifier construction algorithms adopt classification accuracy as performance metrics in model evaluation. Moreover, equal weighting is often applied to each data sample in parameter estimation.
Hong, X., Harris, C.J., Chen, S.
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Predicting Financial Bubbles with Imbalanced Data [PDF]
ITM Web of ConferencesThis paper explores the prediction of financial bubbles within the S&P 500 using a machine learning framework as well as econometric bubble detection tests.
Bogoslavskiy Alexei
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A New Big Data Model Using Distributed Cluster-Based Resampling for Class-Imbalance Problem
Applied Computer Systems, 2019 The class imbalance problem, one of the common data irregularities, causes the development of under-represented models. To resolve this issue, the present study proposes a new cluster-based MapReduce design, entitled Distributed Cluster-based Resampling ...
Terzi Duygu Sinanc, Sagiroglu Seref
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