Paper Info
Title
Range Analysis and Applications to Root Causing
Abstract
We propose a supervised learning algorithm whose aim is to derive features that explain the response variable better than the original features. Moreover, when there is a meaning for positive vs negative samples, our aim is to derive features that explain the positive samples, or subsets of positive samples that have the same root-cause. Each derived feature represents a single or multi-dimensional subspace of the feature space, where each dimension is specified as a feature-range pair for numeric features, and as a feature-level pair for categorical features. Unlike most Rule Learning and Subgroup Discovery algorithms, the response variable can be numeric, and our algorithm does not require a discretization of the response. The algorithm has been applied successfully to numerous real-life root-causing tasks in chip design, manufacturing, and validation, at Intel.
Year
DOI
Venue
2019
10.1109/DSAA.2019.00045
2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA)
Keywords
Field
DocType
feature selection,rule learning,subgroup discovery,range analysis
Discretization,Feature vector,Subspace topology,Feature selection,Pattern recognition,Categorical variable,Computer science,Integrated circuit design,Artificial intelligence,Supervised training,Range analysis
Conference
ISSN
ISBN
Citations 
2472-1573
978-1-7281-4494-8
0
PageRank 
References 
Authors
0.34
8
2
Name
Order
Citations
PageRank
Zurab Khasidashvili130725.40
Adam J. Norman200.68