The increasing digitization and interconnection of legacy Industrial Control Systems (ICSs)
open new vulnerability surfaces, exposing such systems to malicious attackers. Furthermore …

Boosting is a general method for improving the accuracy of any given learning algorithm.
Focusing primarily on the AdaBoost algorithm, this chapter overviews some of the recent …

The extraction of useful insights from text with various types of statistical algorithms is
referred to as text mining, text analytics, or machine learning from text. The choice of …

As one of the most comprehensive machine learning texts around, this book does justice to
the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's …

Class imbalance is a problem that is common to many application domains. When examples
of one class in a training data set vastly outnumber examples of the other class (es) …

Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and
methods for mining patterns, knowledge, and models from various kinds of data for diverse …

Abstract In an earlier paper [9], we introduced a new “boosting” algorithm called AdaBoost
which, theoretically, can be used to significantly reduce the error of any learning algorithm …

Witten and Frank's textbook was one of two books that 1 used for a data mining class in the
Fall of 2001. The book covers all major methods of data mining that produce a knowledge …

Boosting is a general method for improving the accuracy of any given learning algorithm.
This short overview paper introduces the boosting algorithm AdaBoost, and explains the …

Many existing rule learning systems are computationally expensive on large noisy datasets.
In this paper we evaluate the recently-proposed rule learning algorithm IREP on a large and …