Fuzzy C-Means Clustering Based Deep Patch Learning With Improved Interpretability for Classification Problems
نویسندگان
چکیده
Grid partitioning for input space results in the exponential rise number of rules adaptive network-based fuzzy inference system (ANFIS) and patch learning (PL) as features increases, thus resulting huge computational load deteriorating its interpretability. An improved PL (iPL) is put forward training each sub-fuzzy to overcome rule-explosion problem. In iPL, done using c-means (FCM) clustering avoid heavy complexity arising due large generated from high dimensionality. this paper, two novel classifiers, called FCM based deep with high-level interpretability classification problems, are presented, named HI-FCMDPL-CP1 HI-FCMDPL-CP2. The proposed classifiers have characteristics: One a stacked structure component iPL accuracy, other use maximal information coefficient (MIC) maximum misclassification threshold (MMT) optimize structures. High achieved at layer by clustering, concise MMT, random (RI) parameter sharing (PS) integrated improve their accuracy without losing Experiments on several real-word datasets demonstrated that MIC, RI PS HI-FCMDPL-CP2 effective individually, integrating them all three can further performance. A more obtained reduced simultaneously. Furthermore, used determine advantages disadvantages serial versus parallel structures subjective selection these categories.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2022
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2022.3171109