• Sachin Kumar Soni
• Pratima Gautam

Imbalanced data Classification with effective form is a major area of the research, as an imbalance of high class gets certainly integral in several applications of real-world, for example, cancer detection, fraud detection, etc. Additionally, imbalanced data of high form have difficulty, where more than few learners exhibit bias in the direction of the class of majority type, and in the major cases, it possibly will discount minority class in total. Imbalance Class has been considered thoroughly over the last few decades by making use of traditional ML models, that is learning of non-deep form. There is very little research work in the field of DL of class imbalance, despite recent advances in DL, along with its growing popularity. Record-breaking outcomes in several diverse domains have been obtained, and it is of great interest to examine the application of deep neural networks for issues involving high-class imbalances. The research is surveyed to improved comprehend the effectiveness of deep learning (DL) as functional to the imbalanced data of classes. In this paper, the Recursive feature elimination technique is applied followed by the Cat boost algorithm on 3 different datasets which Wisconsin, Bupa, and Pima. The results show the accuracy achieved by the proposed algorithm is better as compared to the existing techniques

Class distribution, Imbalanced dataset, imbalance ratio, binary class, SVM, Cat boost

"Recursive feature elimination technique for Classifying Binary Problem in Imbalanced Datasets ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.2071-2074, April-2019, Available :http://www.jetir.org/papers/JETIR1904S99.pdf

"Recursive feature elimination technique for Classifying Binary Problem in Imbalanced Datasets ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp2071-2074, April-2019, Available at : http://www.jetir.org/papers/JETIR1904S99.pdf