Abstract
Class imbalance is one of the main problem using different algorithms used in machine learning. In imbalance classification of data the false negative is always high. The researchers have introduced many methods to deal with this problem, but the purpose of this paper is to apply machine learning algorithms under the SMOTE and cost sensitive learning approaches and acquired the results from the different experiments to find out the suitable results for imbalanced data.
Highlights
In binary classification, class imbalance can be elaborated as majority class outnumbering of the minority class
The major aim of cost sensitive learning method is to have a hypothesis that reduces the cost on the data
It is beneficial for sampling methods it provides a feasible alternative to sampling methods for imbalanced data
Summary
Class imbalance can be elaborated as majority class outnumbering of the minority class. This can be seen in many machine learning and data mining application such as fraud detection smoke detection (people ought to smoking) and many more (Nitesh et al, 2004). Class imbalance is considered as ten challenging problem in data mining. In a binary classification problem, majority under sampling removes instances from the majority i.e. larger class, with the aim of improving bias of the minority class instances. Random under sampling is an effective technique in which a portion of the majority class instances are removed at random from the dataset (Jason et al, 2007; Gary et al, 2004; Gary et al, 2003)
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