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- W1972047634 abstract "Abstract A popular prediction tool for decision making is the compensatory model based on regression analysis. In this model, the predicted performance is given by a linear combination of predictor scores, so that a reduction on the score of one variable can be compensated for by an increase in the score of a second variable. As an alternative this paper explores the predictive value of two simple Boolean models in which the predicted scores are given by predictor values connected by the or and and operators through the vehicle of fuzzy-set theory. Paramatized transformations relating raw scores on predictors to fuzzy-set membership are introduced and optimal values found. The model is tested on the performance scores of students in a graduate library school program. It is found that on the learning data, the disjunctive model outperforms the traditional regression model; the two models perform about the same on validation data." @default.
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- W1972047634 date "1985-11-01" @default.
- W1972047634 modified "2023-09-26" @default.
- W1972047634 title "A parametric fuzzy-set prediction model" @default.
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- W1972047634 doi "https://doi.org/10.1016/0165-0114(85)90051-x" @default.
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