A Review on the Effect of Imbalanced Dataset on Linear Discriminant Analysis.

Abstract:

Imbalanced data are often delegate issues in data set as it has the power to affect the result and the performance of the classification algorithm. In such problems, if not handled well with a good sampling techques could lead to biased result, overfitting as well as high rate of misclassification thereby favouring just one class among the two classes. Usually, when assigning a sampling techniques, it is necessary to look at the nature of the dataset being studied. It is of a truth that the LDA classifier looking for an efficient performance when presented with an imbalanced instances are not suitable to deal with imbalanced learning tasks, since they tend to classify all the data into the majority class, which is usually the less important class. This work explains the different approaches which have been employed by different researchers to resolve the issue of imbalanced data in LDA and the effect of the result obtained both positively and negatively. It should be noted that this single article cannot completely review all the works or researches done on the topic, hence we hope that the references which was dually cited will be of help to the major theoretical issues.