How can you use feature engineering to handle class imbalance in your dataset?

Resampling methods are commonly used to address class imbalance. These methods involve either oversampling the minority class or undersampling the majority class, or a combination of both. Oversampling creates synthetic minority class samples using techniques like SMOTE or ADASYN, while undersampling reduces majority class samples using methods such as random undersampling, cluster centroids, or Tomek links. Resampling can balance class distribution and reduce model bias but has drawbacks like overfitting, loss of information, and increased computational cost.

Assume that, you have a jar full with balls. The balls are in two colours. One ball is in red color and other one is of blue colour. There are only a few red color balls and all the remaining are in blue balls. You want to have equal probability of drawing both balls from that jar. There are two ways to solve this issue. One way is to keep less number of blue balls, which is underdamping. Here you will reduce occurrence instance of major class. Otherwise, you can put more dummy balls in red color. It is known as over-sampling. In over sampling techniques like SMOTE, we generate more instance of minority class using algorithms like KNN. KNN computes similarity and generate similar data points which increase occurrence of minority class.

Weighting methods are employed to address class imbalance by assigning varying weights to different classes, considering their frequency or significance. These methods can be applied at the data or model level. At the data level, you can adjust features of the minority class based on their importance or use weighted loss functions that penalize minority class errors more. At the model level, algorithms like logistic regression, decision trees, or support vector machines that support class weights can be utilized, with weights adjusted as needed. Weighting methods can enhance model accuracy and recall but may pose challenges like selecting optimal weights, handling noise, or addressing multi-class problems.

Considering that in dataframe, features are represented by the columns and class distribution is represented by the ratio of number of rows, which belongs to one class. Feature selection alone won't change class distribution or balance the odd distribution among classes. Feature selection should be used along with sampling technique. The approach is inspired by RandomForest. Random forest uses subspace sampling, where subset of data points(rows) and features(columns) are selected and decision tree is created based on selected rows and columns. Randomforest creates multiple sub-trees so, class distribution is balanced in some sub-trees. In Similar fashion, feature selection can be used along with sampling technique in controlled manner.

Feature Extraction can not directly balance class distribution. Lets Assume that, you are working on image classification. Image dataset is divided into two set, dog and cat Images. Suppose, you are a dog lover, Hence, you have more dog images and a few cat images. So dataset is imbalanced. If you extract more features(pixel, corners) from cat images and less number of features from dog images. It won't balance dataset. In Adverse condition, dog classification can face over-fitting issues and cat classification can face under-fitting issues. But, In case we group/cluster images based on feature similarity and select images from each cluster. It can balance imbalanced dataset. So feature extraction alone is not a effective approach.

Feature extraction methods offer a fourth approach to address class imbalance by creating new features from existing ones. These techniques transform data into lower or higher-dimensional spaces to capture underlying class patterns. They can be linear or nonlinear, supervised or unsupervised, and deterministic or probabilistic. Examples include PCA, LDA, autoencoders, kernel methods, and manifold learning. Feature extraction enhances feature quality and model performance but may lead to drawbacks like information loss, reduced interpretability, or scalability issues.

Feature generation methods involve creating new features from external sources like domain knowledge, expert insights, or additional data. These methods enhance and enrich the dataset, providing valuable context and insights. For instance, text mining, natural language processing, or sentiment analysis can generate features from text data, while image processing and computer vision can extract features from images. Geospatial analysis, network analysis, and graph mining can create features from spatial data. Feature generation diversifies and improves data, enhancing model accuracy and recall. However, challenges include data quality, availability, and integration issues.