Proposing a New Method for Non-relative Imbalanced Dataset
A well-known domain in that it is highly likely for each exemplary dataset to be imbalanced is patient detection. In such systems there are many clients while a few of them are patient and the all others are healthy. So it is very common and likely to face an imbalanced dataset in such a system that is to detect a patient from various clients. In a breast cancer detection that is a special case of the mentioned systems, it is tried to discriminate the patient clients from healthy clients. It should be noted that the imbalanced shape of a dataset can be either relative or non-relative. The imbalanced shape of a dataset is relative where the mean number of samples is high in the minority class, but it is very less rather than the number of samples in the majority class. The imbalanced shape of a dataset is non-relative where the mean number of samples is low in the minority class. This paper presents an algorithm which is well-suited for and applicable to the field of non-relative imbalanced datasets. It is efficient in terms of both of the speed and the efficacy of learning. The experimental results show that the performance of the proposed algorithm outperforms some of the best methods in the literature.
KeywordsImbalanced Learning Decision Tree Artificial Neural Networks Breast Cancer Detection
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