Constrained Classification of Large Imbalanced Data by Logistic Regression and Genetic Algorithm

Abstract

Abstract-Imbalance in data classification is a frequently discussed problem that is not well handled by classical classification techniques. The problem we tackled was to learn binary classification model from large data with accuracy constraint for the minority class. We propose a new meta-learning method that creates initial models using cost-sensitive learning by logistic regression and uses these models as initial chromosomes for genetic algorithm. The method has been successfully tested on a large real-world data set from our internet security research. Experiments prove that our method always leads to better results than usage of logistic regression or genetic algorithm alone. Moreover, this method produces easily understandable classification model.

Boosting Minority Class Prediction on Imbalanced Point Cloud Data

“…This involves either oversampling instances of the minority class or undersampling instances of the majority class. Oversampling involves the random duplication of instances from minority classes [15][16][17]. Undersampling involves the random removal of instances from majority classes.…”

“…Another strategy is the threshold-moving technique in which the decision threshold is shifted in a manner that reduces bias towards the negative class [15][16][17]26]. It applies to classifiers that, given an input tuple, return a continuous output value.…”

Workplace innovation and social innovation: an introduction

“…This often results in poorly estimated independent variable coefficients. One way to compensate is to under sample the majority class to rebalance the overall sample (Hostla et al, 2013). To maximise the number of observed data points used in the logistic regressions, we used all data points in the 'teams' category and randomly selected an equal number of data points in the 'no teams' category.…”

Decision Tree Algorithm with Class Overlapping-Balancing Entropy for Class Imbalanced Problem

“…Many methods have been presented to deal with the class imbalanced problem using various techniques [10], [11]. The idea of developing the algorithm to build the decision tree classifier that is suitable for classifying an imbalanced dataset is one of the methods that have received wide attention.…”