Classification of Imbalanced Data Using Multilayer Perceptrons

Data classification with multilayer perceptrons using a generalized error function

The learning process of a multilayer perceptron requires the optimization of an error function E(y,t) comparing the predicted output, y, and the observed target, t. We review some usual error functions, analyze their mathematical properties for data classification purposes, and introduce a new one, E(Exp), inspired by the Z-EDM algorithm that we have recently proposed. An important property of ...

Model following using multilayer perceptrons

Optimized multilayer perceptrons for molecular classification and diagnosis using genomic data

MOTIVATION Multilayer perceptrons (MLP) represent one of the widely used and effective machine learning methods currently applied to diagnostic classification based on high-dimensional genomic data. Since the dimensionalities of the existing genomic data often exceed the available sample sizes by orders of magnitude, the MLP performance may degrade owing to the curse of dimensionality and over-...

On Mining Fuzzy Classification Rules for Imbalanced Data

Fuzzy rule-based classification system (FRBCS) is a popular machine learning technique for classification purposes. One of the major issues when applying it on imbalanced data sets is its biased to the majority class, such that, it performs poorly in respect to the minority class. However many cases the minority classes are more important than the majority ones. In this paper, we have extended ...

Parallel Perceptrons and Training Set Selection for Imbalanced Classification Problems

Parallel perceptrons are a novel approach to the study of committee machines that allows, among other things, for a fast training with minimal communications between outputs and hidden units. Moreover, their training allows to naturally define margins for hidden unit activations. In this work we shall show how to use those margins to perform subsample selections over a given training set that r...