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We present an ensemble method for concept drift that dynamically creates and removes weighted experts in response to changes in performance. The method, dynamic weighted majority (DWM), uses four mechanisms to cope with concept drift: It trains online learners of the ensemble, it weights those learners based on their performance, it removes them, also based(More)
Algorithms for tracking concept drift are important for many applications. We present a general method based on the Weighted Majority algorithm for using any on-line learner for concept drift. Dynamic Weighted Majority (dwm) maintains an ensemble of base learners, predicts using a weighted-majority vote of these " experts " , and dynamically creates and(More)
We describe the use of machine learning and data mining to detect and classify malicious exe-cutables as they appear in the wild. We gathered 1, 971 benign and 1, 651 malicious executables and encoded each as a training example using n-grams of byte codes as features. Such processing resulted in more than 255 million distinct n-grams. After selecting the(More)
The problem of learning from imbalanced data sets, while not the same problem as learning when misclassification costs are unequal and unknown, can be handled in a similar manner. That is, in both contexts, we can use techniques from roc analysis to help with classifier design. We present results from two studies in which we dealt with skewed data sets and(More)
To cope with concept drift, we paired a stable online learner with a reactive one. A stable learner predicts based on all of its experience, whereas are active learner predicts based on its experience over a short, recent window of time. The method of paired learning uses differences in accuracy between the two learners over this window to determine when to(More)