Abstract:
Instance-based learning (IBL) algorithms have proved to be successful in many applications. However, as opposed to standard statistical methods, a prediction in IBL is usually given without characterizing its confidence. In this paper, we propose an IBL method that allows for deriving set-valued predictions that cover the correct answer (label) with high probability. Our method makes use of a formal model of the heuristic inference principle suggesting that similar instances do have similar labels. The focus of this paper is on the prediction of numeric values (regression), even though the method is also useful for classification problems if a reasonable similarity measure can be defined on the set of classes.
Citations
|
5044
|
Statistical Learning Theory
– Vapnik
- 1998
|
|
787
|
Instance-based Learning Algorithms
– Aha, Kibler, et al.
- 1991
|
|
400
|
Towards memory-based reasoning
– Stanfill, Waltz
- 1986
|
|
307
|
Locally weighted learning
– Atkeson, Moore, et al.
- 1997
|
|
121
|
Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms
– Aha
- 1992
|
|
41
|
Constraint classification: a new approach to multiclass classification
– Roth, Har-Paled, et al.
- 2002
|
|
37
|
Instance-Based Prediction of Real-Valued Attributes
– Kibler, Aha, et al.
- 1989
|
|
26
|
Building compact competent case-bases
– Smyth, E
- 1999
|
|
15
|
A review and empirical comparison of feature weighting methods for a class of lazy learning algorithms
– Wettschereck, Aha, et al.
- 1997
|
|
8
|
Reliable classifications with Machine Learning
– Kukar, Kononenko
- 2002
|
|
8
|
Transductive confidence machines for pattern recognition
– Proedrou, Nouretdinov, et al.
- 2002
|
|
2
|
Focusing search by using problem solving experience
– Hüllermeier
- 2000
|
|
1
|
Instance-based learning of credible label sets
– Hüllermeier
- 2003
|