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A novel wavelet-based feature extraction approach is introduced in this paper for subject recognition utilizing ground reaction force (GRF) measurements. A wavelet-packet (WP) decomposition scheme is firstly proposed to recognize the discriminating frequency subbands and subsequently an efficient feature selection (FS) method is applied on the selected WP(More)
A novel fuzzy decision tree-based SVM (FDT-SVM) classifier is proposed in this paper, to distinguish between asymptotic (AS) and osteoarthritis (OA) knee gait patterns and to investigate OA severity using 3-D ground reaction force (GRF) measurements. FDT-SVM incorporates effective techniques for feature selection (FS) and class grouping (CG) at each(More)
An efficient wavelet-based feature selection (FS) method is proposed in this paper for subject recognition using ground reaction force measurements. Our approach relies on a local fuzzy evaluation measure with respect to patterns that reveal the adequacy of data coverage for each feature. Furthermore, FS is driven by a fuzzy complementary criterion (FuzCoC)(More)
A Boosted Genetic Fuzzy Classifier (BGFC) is presented in this paper for land cover classification from VHR multispectral images. The proposed system is constructed through a two stage process. The first stage incrementally generates a fuzzy rule base, through repeated invocations of an evolutionary rule extracting algorithm. A boosting algorithm is(More)
In authors' previous works, a novel self-organizing neuro-fuzzy multilayered classifier (SONeFMUC) was proposed. SONeFMUC is composed of small-scale interconnected fuzzy neuron classifiers (FNCs) arranged in layers. The structure of the classifier is revealed by means of the well known GMDH algorithm. In addition, the GMDH algorithm inherently implements(More)