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Missing values are a common occurrence in a number of real world databases, and various statistical methods have been developed to address this problem, which is referred to as missing data imputation. In the detection and prediction of incipient faults in power transformers using Dissolved Gas Analysis (DGA), the problem of missing values is influential and has resulted in inconclusive decision-making...
We discuss uncertainty included in demands in facility location problem in this paper. The uncertain demand is named as fuzzy demand in the paper. In the facility location model, the parameters of fuzzy demand are determined by calculating the estimated expected value of the fuzzy demand, which is obtained by using estimated parameters of underlying probability distribution function of fuzzy data...
In real option pricing, it is impractical to assume the net present value of expected cash flow payoff as an exact number because it is a forecasted vague one. The price can be defined as a fuzzy number to express its estimated uncertain values and the Binomial Tree is used to price a real option. A modified pricing approach to real options is thus proposed to transform the forecasted uncertain values...
One of the main difficulties in real-world data classification and analysis tasks is that the data distribution can be imbalanced. In this paper, a variant of the supervised learning neural network from the Adaptive Resonance Theory (ART) family, i.e., Fuzzy ARTMAP (FAM) which is equipped with a conflict-resolving facility, is proposed to classify an imbalanced dataset that represents a real problem...
A multi-level decision making problem confronts several issues especially in coordinating decision in hierarchic processes and in compromising conflicting objectives for each decision level. Therefore, its mathematical model plays a pivotal role in solving such problem, and is influencing to the final result. However, it is sometimes difficult to estimate the coefficients of objective functions of...
It is sometimes difficult in real situations to estimate the coefficients of decision variables in multi-objective model. Even though mathematical analysis may contribute to determine these coefficients, historical data used may contain fuzzy and random properties and should be treated properly. Thus, this paper introduces a fuzzy random regression to approximate the coefficients; specifically the...
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