An Improved Rectangular Decomposition Algorithm for Imprecise and Uncertain Knowledge Discovery
نویسندگان
چکیده
In this paper, we propose a novel improved algorithm for the rectangular decomposition technique for the purpose of performing fuzzy knowledge discovery from large scaled database in a dynamic environment. To demonstrate its effectiveness, we compare the proposed one which is based on the newly derived mathematical properties with those of other methods with respect to the classification rate, the number of rules, and complexity analysis.
منابع مشابه
Updating finite element model using frequency domain decomposition method and bees algorithm
The following study deals with the updating the finite element model of structures using the operational modal analysis. The updating process uses an evolutionary optimization algorithm, namely bees algorithm which applies instinctive behavior of honeybees for finding food sources. To determine the uncertain updated parameters such as geometry and material properties of the structure, local and...
متن کاملA Lagrangian Decomposition Algorithm for Robust Green Transportation Location Problem
In this paper, a green transportation location problem is considered with uncertain demand parameter. Increasing robustness influences the number of trucks for sending goods and products, caused consequently, increase the air pollution. In this paper, two green approaches are introduced which demand is the main uncertain parameter in both. These approaches are addressed to provide a trade-off b...
متن کاملA Fuzzy Rule-based Expert System for the Prognosis of the Risk of Development of the Breast Cancer
Soft Computing techniques play an important role for decision in applications with imprecise and uncertain knowledge. The application of soft computing disciplines is rapidly emerging for the diagnosis and prognosis in medical applications. Between various soft computing techniques, fuzzy expert system takes advantage of fuzzy set theory to provide computing with uncertain words. In a fuzzy exp...
متن کاملEvidential-EM Algorithm Applied to Progressively Censored Observations
Evidential-EM (E2M) algorithm is an effective approach for computing maximum likelihood estimations under finite mixture models, especially when there is uncertain information about data. In this paper we present an extension of the E2M method in a particular case of incomplete data, where the loss of information is due to both mixture models and censored observations. The prior uncertain infor...
متن کاملAn Algorithm to Obtain Possibly Critical Paths in Imprecise Project Networks
We consider criticality in project networks having imprecise activity duration times. It is well known that finding all possibly critical paths of an imprecise project network is an NP-hard problem. Here, based on a method for finding critical paths of crisp networks by using only the forward recursion of critical path method, for the first time an algorithm is proposed which can find all pos...
متن کامل