Abstract
Index System Reduction Method Based on the Index Similarity
Highlights
Multi-attribute decision making (MADM) is a subfield of operations research, concerned with selecting the best alternative through the evaluation of the whole set of attributes which are hard to quantify, incommensurable or incomparable [2, 35]
In order to verify the effectiveness of the proposed ISRS, we used random number generation to create the dataset with eight indexes and fourteen samples
The index reduction effect of principal component analysis (PCA) is stronger than the ISRS; (2) the index meaning of the reduced index system OOPPPPPPis much more complicated than OOIIIIIIII, besides, OOPPPPPP contains negative value while both the initial index system S and OOIIIIIIII do not exist
Summary
Multi-attribute decision making (MADM) is a subfield of operations research, concerned with selecting the best alternative through the evaluation of the whole set of attributes which are hard to quantify, incommensurable or incomparable [2, 35]. A number of redundant or relational attributes (indexes) might increase the potential internal inconsistency and computational complexity of the MADM methods, such as analytic hierarchy process (AHP) [37, 39]. To deal with this drawback, an appropriate index reduction should be implemented. Since an index system is exactly a system which consists of different indexes (elements) with specific structure (relation), the index system reduction problem could be definitely transformed to the system structure partition problem, that is selecting the most representative index from each index subsystem. Here,XXiiii ∈ SSii ⊆ SS is an index and the final reduced index system OO ⊆ SS
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