Qualitative test-cost sensitive classification
Author
Cebe, Mümin
Advisor
Demir, Çiğdem Gündüz
Date
2008Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Show full item recordAbstract
Decision making is a procedure for selecting the best action among several
alternatives. In many real-world problems, decision has to be taken under the
circumstances in which one has to pay to acquire information. In this thesis, we
propose a new framework for test-cost sensitive classification that considers the
misclassification cost together with the cost of feature extraction, which arises
from the effort of acquiring features. This proposed framework introduces two
new concepts to test-cost sensitive learning for better modeling the real-world
problems: qualitativeness and consistency.
First, this framework introduces the incorporation of qualitative costs into
the problem formulation. This incorporation becomes important for many real
world problems, from finance to medical diagnosis, since the relation between
the misclassification cost and the cost of feature extraction could be expressed
only roughly and typically in terms of ordinal relations for these problems. For
example, in cancer diagnosis, it could be expressed that the cost of misdiagnosis
is larger than the cost of a medical test. However, in the test-cost sensitive classification
literature, the misclassification cost and the cost of feature extraction
are combined quantitatively to obtain a single loss/utility value, which requires
expressing the relation between these costs as a precise quantitative number.
Second, the proposed framework considers the consistency between the current
information and the information after feature extraction to decide which features
to extract. For example, it does not extract a new feature if it brings no new
information but just confirms the current one; in other words, if the new feature
is totally consistent with the current information. By doing so, the proposed
framework could significantly decrease the cost of feature extraction, and hence,
the overall cost without decreasing the classification accuracy. Such consistency behavior has not been considered in the previous test-cost sensitive literature.
We conduct our experiments on three medical data sets and the results demonstrate
that the proposed framework significantly decreases the feature extraction
cost without decreasing the classification accuracy.
Keywords
Cost-sensitive learningQualitative decision theory
Feature extraction cost
Feature selection
Decision theory