In the proposed EFC (Explainable Feature Construction) system, we construct various types of features: operator-based features (using logical, relational, Cartesian, and numerical operators), features from rule learning (H�hn and H�llermeier, 2009), and features based on a threshold for the presence of several features ...
3 Explanation-based Feature Construction. In classical EBL, an “explanation” is a logical proof that shows how the class label of a particular labeled�...
We describe an approach to feature construction where task-relevant discriminative features are automatically constructed, guided by an explanation-based�...
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This work describes an approach to feature construction where task-relevant discriminative features are automatically constructed,�...
We propose algorithms for automated feature construction where available domain knowledge, even though imperfect and approximate, can be utilized by the�...
We describe an approach to feature construction where task-relevant discriminative features are automatically constructed, guided by an explanation-based�...
TL;DR: This work describes an approach to feature construction where task-relevant discriminative features are automatically constructed,�...
Jan 23, 2023 � The proposed Explainable Feature Construction (EFC) methodology identifies groups of co- occurring attributes exposed by popular explanation�...
Feature construction is the process of creating new features based on existing ones to detect and model interactions in data. It involves defining new features�...
Jan 24, 2023 � Feature construction is an essential step in the data science process that involves creating new features from existing ones.
