in the classical data envelopment analysis (dea) models, inputs and outputs are assumed as known variables, and these models cannot deal with unknown amounts of variables directly. in recent years, there are few researches on handling missing data. this paper suggests a new interval based approach to apply missing data, which is the modified version of kousmanen (2009) approach. first, the prop...

In the classical data envelopment analysis (DEA) models, inputs and outputs are assumed as known variables, and these models cannot deal with unknown amounts of variables directly. In recent years, there are few researches on handling missing data. This paper suggests a new interval based approach to apply missing data, which is the modified version of Kousmanen (2009) approach. First, the prop...

Bayesian optimization (BO) is an efficient method for optimizing expensive black-box functions. In real-world applications, BO often faces a major problem of missing values in inputs. The inputs can happen two cases. First, the historical data training contain values. Second, when performing function evaluation (e.g., computing alloy strength heat treatment process), errors may occur thermostat...

For the purposes of this paper, data mining involves: Predictive modeling (Estimating parameters is not of major interest) Nonnormal data with nonlinear relationships Large data sets This paper will not cover issues regarding small data sets, such as Bayesian predictive distributions, or tree-based models, for which specialized methods are available for handling missing data. Missing data are a...

Traditional data envelopment analysis (DEA) models evaluate two-stage decision making unit (DMU) as a black box and neglect the connectivity may exist among the stages. This paper looks inside the system by considering the intermediate activities between the stages where the first stage uses inputs to produce outputs which are the inputs to the second stage along with its own inputs. Additional...

data envelopment analysis (dea) is a method for measuring the efficiency of peer decision making units (dmus) with multiple inputs and outputs. the traditional dea treats decision making units under evaluation as black boxes and calculates their efficiencies with first inputs and last outputs. this carries the notion of missing some intermediate measures in the process of changing the inputs to...

Data Envelopment Analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs) with multiple inputs and outputs. The traditional DEA treats decision making units under evaluation as black boxes and calculates their efficiencies with first inputs and last outputs. This carries the notion of missing some intermediate measures in the process of changing the inputs to...