Sampling is used for data preprocessing so as to avoid misclassification or wrong information.
Yes. Sampling techniques like oversaming, under sampling, Near Miss etc are useful in hamdling imbalanced data. With sampling techniques you can avoid biasness in model prediction towards majority class of target variable
Chitra G Desai thank you for your response. I would like to know if there are better techniques for imbalanced data such as cost-sensitive learning , estimation of cost function etc. Are these techniques much better than oversampling / undersampling or do they go hand-in-hand with their performance? Is there is any specialty of each of them to find some specific use or can they be generally applied or randomly chosen? Do these techniques depend on the kind of architecture chosen?
General sampling would not work in imbalanced data. Smart sampling is required to optimize information such as variable sampling rate. The time interval in next sample should be short if a sample indicate change in the information rate and long if there is no indication of a change. It can be measured by information characteristics.
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