Mine Classification with Imbalanced Data

In binary classification problems it is common for the two classes to be imbalanced: one case is very rare compared to the other. Traditional classification approaches usually ignore this class imbalance, causing performance to suffer accordingly. In contrast, the algorithm infinitely imbalanced logistic regression (IILR) algorithm explicitly addresses class imbalance in its formulation. This project considers the infinitely imbalanced case where one class has a finite sample size and the other class’s sample size grows without bound. For logistic regression, the infinitely imbalanced case often has a useful solution. Under mild conditions, the intercept diverges as expected, but the rest of the coefficient vector approaches a non trivial and useful limit. That limit can be expressed in terms of exponential tilting and is the minimum of a convex objective function. The limiting form of logistic regression suggests a computational shortcut for fraud detection problems. IILR algorithm gives the details necessary to employ it for Remote sensing-collision data sets characterized by class imbalance .The method is applied to the problem of mine classification on real, measured data sets. Specifically, classification performance using the IILR algorithm is exceeding that of a standard logistic regression approach.