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On Graph Kernels: Hardness Results and Efficient Alternatives
As most ‘real-world’ data is structured, research in kernel methods has begun investigating kernels for various kinds of structured data, but only very specific graphs such as trees and strings have been considered.
A kernel on multi-instance data that can be shown to separate positive and negative sets under natural assumptions is shown and compares favorably with state of the art multi- instance learning algorithms in an empirical study.
Subgroup Discovery with CN2-SD
- N. Lavrac, B. Kavšek, Peter A. Flach, L. Todorovski
- Computer ScienceJ. Mach. Learn. Res.
- 1 December 2004
A subgroup discovery algorithm, CN2-SD, developed by modifying parts of the CN2 classification rule learner: its covering algorithm, search heuristic, probabilistic classification of instances, and evaluation measures, shows substantial reduction of the number of induced rules, increased rule coverage and rule significance, as well as slight improvements in terms of the area under ROC curve.
The Geometry of ROC Space: Understanding Machine Learning Metrics through ROC Isometrics
- Peter A. Flach
- Computer ScienceICML
- 21 August 2003
The paper demonstrates that the graphical depiction of machineLearning metrics by means of ROC isometrics gives many useful insights into the characteristics of these metrics, and provides a foundation on which a theory of machine learning metrics can be built.
Rule Evaluation Measures: A Unifying View
This paper develops a unifying view on some of the existing measures for predictive and descriptive induction by means of contingency tables, and demonstrates that many rule evaluation measures developed for predictive knowledge discovery can be adapted to descriptive knowledge discovery tasks.
Proceedings of the 28th International Conference on Machine Learning
Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration
- Meelis Kull, Miquel Perelló-Nieto, Markus Kängsepp, Telmo de Menezes e Silva Filho, Hao Song, Peter A. Flach
- Computer ScienceNeurIPS
- 3 September 2019
A natively multiclass calibration method applicable to classifiers from any model class, derived from Dirichlet distributions and generalising the beta calibration method from binary classification is proposed.
Database Dependency Discovery: A Machine Learning Approach
The algorithms in this paper are designed such that they can easily be generalised to other kinds of dependencies, and the bottom-up algorithm is the most efficient of the three, and also outperforms other algorithms from the literature.
Learning Decision Trees Using the Area Under the ROC Curve
This paper shows how a single decision tree can represent a set of classifiers by choosing different labellings of its leaves, or equivalently, an ordering on the leaves, and proposes a novel splitting criterion which chooses the split with the highest local AUC.
Precision-Recall-Gain Curves: PR Analysis Done Right
It is demonstrated experimentally that the area under traditional PR curves can easily favour models with lower expected F1 score than others, and so the use of Precision-Recall-Gain curves will result in better model selection.