Techniques and Tools to Tackle Imbalanced Learning

Data is a prominent factor in machine learning. The quality and depth of data used for training determine the efficiency of our machine learning model. The skewness of data is viewed as one of the pain points in machine learning. This happens in datasets when one class outnumbers the other classes. For predicting better results, the model is trained with balanced data. Researchers had developed different techniques to reduce the skewness of the data. This paper points out the different techniques used for balancing the data. This paper also describes the software's like KEEL, WEKA, R, Python, Multi-Imbalance package and Spark that can be used for data processing and the different algorithms