12,003 research outputs found
Reliable Multi-label Classification: Prediction with Partial Abstention
In contrast to conventional (single-label) classification, the setting of
multilabel classification (MLC) allows an instance to belong to several classes
simultaneously. Thus, instead of selecting a single class label, predictions
take the form of a subset of all labels. In this paper, we study an extension
of the setting of MLC, in which the learner is allowed to partially abstain
from a prediction, that is, to deliver predictions on some but not necessarily
all class labels. We propose a formalization of MLC with abstention in terms of
a generalized loss minimization problem and present first results for the case
of the Hamming loss, rank loss, and F-measure, both theoretical and
experimental.Comment: 19 pages, 12 figure
Integrating Economic Knowledge in Data Mining Algorithms
The assessment of knowledge derived from databases depends on many factors. Decision makers often need to convince others about the correctness and effectiveness of knowledge induced from data.The current data mining techniques do not contribute much to this process of persuasion.Part of this limitation can be removed by integrating knowledge from experts in the field, encoded in some accessible way, with knowledge derived form patterns stored in the database.In this paper we will in particular discuss methods for implementing monotonicity constraints in economic decision problems.This prior knowledge is combined with data mining algorithms based on decision trees and neural networks.The method is illustrated in a hedonic price model.knowledge;neural network;data mining;decision trees
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