On learning guarantees to unsupervised concept drift detection on data streams

Autor(es)

de Mello, Rodrigo F. ^[1] ; Vaz, Yule ^[1] ; Grossi, Carlos H. ^[2] ; Bifet, Albert ^[3]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Math & Comp Sci, Dept Comp Sci, Av Trabalhador Sao Carlense, BR-400 Sao Carlos, SP - Brazil

^[2] Univ Sao Paulo, Inst Math & Comp Sci, Dept Math, Av Trabalhador Sao Carlense, BR-400 Sao Carlos, SP - Brazil

^[3] Telecom ParisTech, Data Intelligence & Graphs Team, LTCI, Off C201 2, 46 Rue Barrault, F-75634 Paris 13 - France

• Tipo de documento: Artigo Científico
• Fonte: EXPERT SYSTEMS WITH APPLICATIONS; v. 117, p. 90-102, MAR 1 2019.
• Citações Web of Science: 4

Resumo

Motivated by the Statistical Learning Theory (SLT), which provides a theoretical framework to ensure when supervised learning algorithms generalize input data, this manuscript relies on the Algorithmic Stability framework to prove learning bounds for the unsupervised concept drift detection on data streams. Based on such proof, we also designed the Plover algorithm to detect drifts using different measure functions, such as Statistical Moments and the Power Spectrum. In this way, the criterion for issuing data changes can also be adapted to better address the target task. From synthetic and real-world scenarios, we observed that each data stream may require a different measure function to identify concept drifts, according to the underlying characteristics of the corresponding application domain. In addition, we discussed about the differences of our approach against others from literature, and showed illustrative results confirming the usefulness of our proposal. (C) 2018 Elsevier Ltd. All rights reserved. (AU)

• Processo FAPESP: 17/16548-6 - Proposta de uma abordagem com garantias teóricas para a detecção de mudanças de conceito em fluxos de dados
• Beneficiário: Rodrigo Fernandes de Mello
• Modalidade de apoio: Bolsas no Exterior - Pesquisa