Multiclass classification of imbalanced data
- 1. MULTICLASS CLASSIFICATION OF IMBALANCED DATA SAURABH WANI 27/04/2019
- 2. What are Imbalanced Datasets ?
- 3. Why do we need to work with Imbalanced Datasets ?
- 4. Applications of Handling Imbalanced Data Terrorist Identification Credit Card Fraud Detection Rare Disease Identification Anomaly Detection
- 5. The MYTH of Accuracy
- 6. Predicted Positive Predicted Negative Actual Positive TP FN Actual Negative FP TN Confusion Matrix 𝐴𝑐𝑐 = 𝑇𝑃 + 𝑇𝑁 𝑇𝑃 + 𝐹𝑃 + 𝑇𝑁 + 𝐹𝑁 𝑃𝑟ecision = 𝑇𝑃 𝑇𝑃 + 𝐹𝑃 𝑅𝑒𝑐𝑎𝑙𝑙 = 𝑇𝑃 𝑇𝑃 + 𝐹𝑁 𝐹1 = 2 ∗ 𝑃 ∗ 𝑅 𝑃 + 𝑅
- 8. Handling Imbalanced Data Resampling Techniques Algorithmic Ensemble Techniques 1. Undersampling 2. Oversampling 3. Clustering based resampling 4. Sample Synthesis 1. Bootstrap Aggregating (Bagging) 2. Boosting 3. Ada-Boost
- 11. Clustering based Undersampling Cluster the majority class, to create a smaller set – replace actual data from majority class. •Can cause accuracy loss for negative cases •Centroids could be randomized
- 13. SMOTE (Synthetic Minority Oversampling Technique)
- 15. Bagging (Bootstrap Aggregating) Bootstrapping The method of randomly assigning k samples out of n samples to a subset with replacement
- 16. • Can be performed on both Regression and Classification • Increase Accuracy and reduce Variance • Reduce the problem of Overfitting The weak learners (Models) are chosen in such a way that each of them specializes in predictions based on one of the feature space.
- 17. Boosting The term ‘Boosting’ refers to a family of algorithms which converts weak learner to strong learners. Boosting is an ensemble method for improving the model predictions of any given learning algorithm. The idea of boosting is to train weak learners sequentially, each trying to correct its predecessor.
- 19. Ada-Boost (Adaptive Boosting) It focuses on classification problems and aims to convert a set of weak classifiers into a strong one. 1. ‘Stumps’ are the weak learners almost every time 2. Each sample has a weight assigned to it (Equal in the beginning) 3. Weights are reassigned based on the accuracy of classification of that specific sample 4. The higher the weight, the more the focus will be on that sample
- 20. Gradient Boosting 1. Instead of adjusting weights in each iteration Gradient Boosting algorithms minimizes the error term each time 2. ‘Gradient Descent’ is used for error correction 3. Highly efficient 4. Really slow
- 21. Thank You