How do you deal with class imbalance and noise in SVM for image classification?

From what I've seen, dealing with class imbalance and noise requires a deep understanding of how the data is stored. Class imbalance often looks like how things are in the real world, where some classes naturally show up more often than others. In these situations, it's important to know that balancing classes isn't always the best idea because you might miss out on important details about the majority class. Noise, on the other hand, can come from a number of places, such as a flawed sensor or things in the environment at the time of the image capture. By learning about these sources, you can make more targeted preprocessing plans to lessen their effects.

In a Imbalanced dataset, the distribution of class is not equal. Weighted or cost-sensitive ML algorithm is used for handling class imbalance issue. Generally, cost or class weights are calculated based on the class distribution of the dataset. Support vector machine has cost-sensitive SVM option, which is effective on imbalanced dataset. In sklearn library, we can pass the weight as a separate parameter in SVM function. SVM computes soft-margin on the basis of weight. A larger weight is used for the minority class, which makes the margin softer for the minority class. A smaller weight is used for majority class, which makes the margin harder and reduces misclassified cases. Weight option provides flexibility to SVM algorithm.

I believe that creating synthetic?data, along with resampling and weighted SVMs, can be a strong way to fix class imbalance. Some methods, such as SMOTE (Synthetic Minority Over-sampling Technique) or its variations, can create synthetic?examples for the minority group. This makes the dataset bigger without losing any useful data. But it's very important to make sure that the synthetic examples are accurate and don't introduce any biases that might hurt the model's performance.

I've realized that feature engineering is critical in dealing with noise in image classification tasks. Before putting data into the SVM, it can be extremely helpful to get rid of noise-prone features that aren't very sensitive. Edge detection or texture analysis, for example, can help you focus on the most important parts of an image, making pixel-level noise less noticeable.

From what I've seen, it's essential to think about the SVM model's context and purpose when choosing evaluation metrics. Some kinds of mistakes may cost more than others in some situations. To give an example, in medical image classification, missing a positive case (high recall) could be worse than misclassifying a negative case (high precision). So, changing the evaluation metric to take into account the costs and benefits of different kinds of errors can give a more accurate picture of how useful the model is.

I like to think of hyperparameter optimization as finding a balance between how complicated the model is and how quickly it can be computed. Grid search and random search are common methods, but they can be hard to run on computers. A more thorough search through the hyperparameter space can be done with more advanced techniques, such as Bayesian optimization. Using domain knowledge to choose the kernel function can also greatly improve the model's performance by making it more in line with the underlying data structure.

In an image classification project involving face detection, the dataset had a large number of easy-to-classify non-face images and a smaller set of hard-to-classify face images. I implemented hard negative mining, where the SVM model initially trained on a balanced dataset, and then I added misclassified non-face images (hard negatives) back into the training set. This forced the SVM to focus on distinguishing between similar-looking non-face and face images, improving both precision and recall. Hard negative mining allows the model to learn from difficult, misclassified examples, making it more flexible and robust against noisy or overlapping classes.