An Integrated AFS-Based SWOT Analysis Approach for Evaluation of Strategies Under MCDM Environment

This article outlines the development of a hybrid methodology aimed to help the policymakers in strategic planning. The proposed methodology integrates the axiomatic fuzzy set (AFS) theory, analytic hierarchy process (AHP), and the concept of simple additive weighting (SAW) to evaluate the strategies by strengths, weaknesses, opportunities, and threats (SWOT) analysis. The combination of AHP with SWOT yields analytically determined weights of the factors included in SWOT analysis. The SAW technique provides a flexible technique to obtain the final ranking of strategies in multi-criteria decision situations. In SAW, the strategies are described using the AFS-based AHP calculation framework for normalization and consistent ratings over the SWOT factors. The AFS theory is incorporated in the model to overcome the uncertainty and ambiguity in human decision-making processes. The proposed integrated methodology copes with the inconsistency caused by different types of fuzzy numbers and normalization methods required in solving multi-criteria decision-making (MCDM) problems. A real-world application is conducted to illustrate the utilization of the model to evaluate SWOT analysis and strategies for tourism development.

New classification technique: fuzzy oblique decision tree

Based on axiomatic fuzzy set (AFS) theory and fuzzy information entropy, a novel fuzzy oblique decision tree (FODT) algorithm is proposed in this paper. Traditional axis-parallel decision trees only consider a single feature at each non-leaf node, while oblique decision trees partition the feature space with an oblique hyperplane. By contrast, the FODT takes dynamic mining fuzzy rules as a decision function. The main idea of the FODT is to use these fuzzy rules to construct leaf nodes for each class in each layer of the tree; the samples that cannot be covered by the fuzzy rules are then put into an additional node – the only non-leaf node in this layer. Construction of the FODT consists of four major steps: (a) generation of fuzzy membership functions automatically by AFS theory according to the raw data distribution; (b) extraction of dynamically fuzzy rules in each non-leaf node by the fuzzy rule extraction algorithm (FREA); (c) construction of the FODT by the fuzzy rules obtained from step (b); and (d) determination of the optimal threshold [Formula: see text] to generate a final tree. Compared with five traditional decision trees (C4.5, LADtree (LAD), Best-first tree (BFT), SimpleCart (SC) and NBTree (NBT)) and a recently obtained fuzzy rules decision tree (FRDT) on eight UCI machine learning data sets and one biomedical data set (ALLAML), the experimental results demonstrate that the proposed algorithm outperforms the other decision trees in both classification accuracy and tree size.