Marriage of fuzzy sets and multiple correspondence analysis: Examples with subjective interval data and biomedical signals
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An innovative integrated modelling of safety data using multiple correspondence analysis and fuzzy discretization techniques
2020, Safety ScienceCitation Excerpt :This transformation process also results in loss of valuable information because the degree of contribution to discretized values are not considered (Hu et al., 2007) which can be alleviated by using fuzzy coding (Zerrin and Greenacre, 2011). Representation of information in terms of linguistic terms in fuzzy coding approach will help in overcoming the drawbacks of traditional data mining based discretization approach (Loslever and Bouilland, 1999). Discretization is carried out in two important steps, data characterization, and data codification.
Variable compensation during the sit-to-stand task among individuals with severe knee osteoarthritis
2017, Annals of Physical and Rehabilitation MedicineCitation Excerpt :Contrary to a classical binary approach, in which only one value can represent a modality for a variable (e.g., low, average, high: 1, 0, 0), the fuzzy approach permits a sliding scale of membership values (e.g., low, average, high: 0.7, 0.3, 0) as the probability of belonging to a modality. This feature improves the handling of data imprecision caused by changing quantitative data into qualitative data [23,36]. Thus, in this study, biomechanical variables were coded and normalized by using 3 triangular fuzzy membership functions: low, average and high [37].
Identification of gait patterns in individuals with cerebral palsy using multiple correspondence analysis
2013, Research in Developmental DisabilitiesCitation Excerpt :The fuzzy window coding normalizes the data from different natures, it simplifies the knowledge extraction process and it increases data interpretability (Chau, 2001a). Contrary to a classical-binary approach in which only one value can represent a modality for a parameter (e.g., Low, Average, High – 1, 0, 0), the fuzzy approach permits different membership values (e.g., Low, Average, High – 0.7, 0.3, 0) as the probability of belonging to a modality and can deal with data imprecision (Bouilland & Loslever, 1998; Loslever & Bouilland, 1999). Thus, the biomechanical parameters were coded and normalized using three triangular fuzzy membership functions related to the following three modalities – Low, Average and High – as used by Armand et al. (2007) and Sagawa et al. (2012).
Associations between gait and clinical parameters in patients with severe knee osteoarthritis: A multiple correspondence analysis
2013, Clinical BiomechanicsCitation Excerpt :To interpret this relationship, multivariate statistical analyses have been proposed. These analyses have been employed to adapt the multidimensionality of clinical and biomechanical data to optimise the extraction of relevant information (for reviews see (Bouilland and Loslever, 1998; Chau, 2001a, 2001b; Loslever and Bouilland, 1999)). Given the complexity of knee OA, a multivariate statistical analysis may be helpful to increase knowledge of the gait in patients with knee OA.
Biplots of fuzzy coded data
2011, Fuzzy Sets and SystemsCorrespondence analysis with fuzzy data: The fuzzy eigenvalue problem
2007, Fuzzy Sets and Systems