1.6k citations · 4.7k across the 46 of their papers we have counts for
6 papers · 1 filter
Specious rules: an efficient and effective unifying method for removing misleading and uninformative patterns in association rule mining
Wilhelmiina Hämäläinen, Geoffrey I. Webb
We present theoretical analysis and a suite of tests and procedures for addressing a broad class of redundant and misleading association rules we call \emph{specious rules}. Specio…
A Tutorial on Statistically Sound Pattern Discovery
Wilhelmiina Hämäläinen, Geoffrey I. Webb
Statistically sound pattern discovery harnesses the rigour of statistical hypothesis testing to overcome many of the issues that have hampered standard data mining approaches to pa…
Accurate parameter estimation for Bayesian Network Classifiers using Hierarchical Dirichlet Processes
Francois Petitjean, Wray Buntine, Geoffrey I. Webb +1
This paper introduces a novel parameter estimation method for the probability tables of Bayesian network classifiers (BNCs), using hierarchical Dirichlet processes (HDPs). The main…
Understanding Concept Drift
Geoffrey I. Webb, Loong Kuan Lee, François Petitjean +1
Concept drift is a major issue that greatly affects the accuracy and reliability of many real-world applications of machine learning. We argue that to tackle concept drift it is im…
Controlling Familywise Error When Rejecting at Most One Null Hypothesis Each From a Sequence of Sub-Families of Null Hypotheses
Geoffrey I. Webb, Mark van der Laan
We present a procedure for controlling FWER when sequentially considering successive subfamilies of null hypotheses and rejecting at most one from each subfamily. Our procedure dif…
On the Effectiveness of Discretizing Quantitative Attributes in Linear Classifiers
Nayyar A. Zaidi, Yang Du, Geoffrey I. Webb
Learning algorithms that learn linear models often have high representation bias on real-world problems. In this paper, we show that this representation bias can be greatly reduced…