Range Analysis and Applications to Root Causing
| Autor: | Zurab Khasidashvili, Adam J. Norman |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Discretization business.industry Computer science Feature vector Feature selection Pattern recognition 0102 computer and information sciences 01 natural sciences 03 medical and health sciences 030104 developmental biology Dimension (vector space) 010201 computation theory & mathematics Feature (machine learning) Artificial intelligence business Categorical variable Subspace topology Variable (mathematics) |
| Zdroj: | DSAA |
| DOI: | 10.1109/dsaa.2019.00045 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|