.png){kind=icon}
Return
A Decision-Theoretic Rough Set Approach for Dynamic Data Mining
.png){kind=icon}
PRE
AI
DOI：10.1109/TFUZZ.2014.2387877.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Uncertainty and fuzziness generally exist in real-life data. Approximations are employed to describe the uncertain information approximately in rough set theory. Certain and uncertain rules are induced directly from different regions partitioned by approximations. Approximation can further be applied to data-mining-related task, e.g., attribute reduction. Nowadays, different types of data collected from different applications evolve with time, especially new attributes may appear while new objects are added. This paper presents an approach for dynamic maintenance of approximations w.r.t. objects and attributes added simultaneously under the framework of decision-theoretic rough set (DTRS). Equivalence feature vector and matrix are defined first to update approximations of DTRS in different levels of granularity. Then, the information system is decomposed into subspaces, and the equivalence feature matrix is updated in different subspaces incrementally. Finally, the approximations of DTRS are renewed during the process of updating the equivalence feature matrix. Extensive experimental results verify the effectiveness of the proposed methods.
Keywords:
Decision-theoretic rough set (DTRS)
granular computing
incremental learning
information system
Journal
IF:
11.9
Papers:
5.0K
Citations:
2.9W
Organization
Cited Papers
Characterization of Two Cactus Formulation-Based Flocculants and Investigation on Their Flocculating Ability for Cationic and Anionic Dyes Removal.png)
Polymers
OAAI
PolymersIF0
OAAIShareSaveShareSaveShareSaveShareSaveShareSaveLate gadolinium enhancement on cardiac magnetic resonance combined with 123I- metaiodobenzylguanidine scintigraphy strongly predicts long-term clinical outcome in patients with dilated cardiomyopathyPLOS ONEOAAI
PLOS ONEIF0
OAAIShareSave
ShareSave
View more