Selective Sampling via Similarity Factor for Sequential Imbalanced Data
Publish place: Fifth National Conference on Computer Engineering
Publish Year: 1400
Type: Conference paper
Language: English
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Document National Code:
TECCONF05_037
Index date: 3 October 2021
Selective Sampling via Similarity Factor for Sequential Imbalanced Data abstract
Considering labeling problems in some online processes, sampling strategies with limited budget would save both time and cost for the operator; while keeping results in an acceptable range. Selective sampling has made supervised learning possible for some expensive or inapplicable online processes. A good sampling strategy give this opportunity to label prediction system to have labels that will improve system’s accuracy. An ideal sampling strategywould query labels of data points that have been classified in wrong class. Sampling problem has common characteristics with anomaly detection problem. This paper proposes a selective sampling method based on principal curves. Proposed algorithm returns a similarity factor between input stream and existing structures of predictor system in each step. No randomness is involved in the proposed algorithm to make it applicable in sensitive real-world problems.
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Selective Sampling via Similarity Factor for Sequential Imbalanced Data authors
Aref Hakimzadeh
School of Electrical and Computer Engineering Shiraz University Shiraz, Fars
Koorush Ziarati
School of Electrical and Computer Engineering Shiraz University Shiraz, Fars