IEEE Transactions on Software Engineering - Special issue on computer security and privacy
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Approximation Schemes in Logic and Artificial Intelligence
Transactions on Rough Sets IX
Transactions on rough sets VI
On generalizing rough set theory
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Simultaneous Anomaly and Misuse Intrusion Detections Based on Partial Approximative Set Theory
PDP '11 Proceedings of the 2011 19th International Euromicro Conference on Parallel, Distributed and Network-Based Processing
Approximation of sets based on partial covering
Transactions on Rough Sets XVI
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Let us assume that we observe a class of objects and have some well-defined features with which an observed object possesses or not. In real life, two relevant groups of objects can be established determined by our current and necessarily constrained knowledge. In particular, a group whose elements really possess a feature in question, and another group whose elements substantially do not possess the same feature. In practice, as a rule, we can observe a feature of objects via only tools with which we are able to judge easily whether an object possesses a property or not. Of course, a property ascertained by tools does not coincide with a feature completely. To manage this problem, we propose a general tool-based approximation framework based on partial approximation of sets in which a positive feature and its negative one of any proportion of the observed objects can simultaneously be approximated.