A Hybrid Technique for Detection and Handling Noise in Binary Classification

Binary classification is a widely utilized method in data mining. However, the presence of noise within the training dataset can significantly impact classification accuracy. Our aim in this study is to identify such noisy data by using polyhedral conic functions. Then the dataset is reconstructed by making the necessary changes to enhance the effectiveness of binary classification studies by improving the quality of the training data.