| Title | An Improved Fuzzy Min–Max Neural Network for Data Classification |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Authors | Kumar, S. A., Kumar, A., Bajaj, V., Singh, G. K. |
| Journal | IEEE Transactions on Fuzzy Systems |
| Volume | 28 |
| Pagination | 1910–1924 |
| Date Published | sep |
| ISSN | 1941-0034 |
| Keywords | Adaptation models, Adaptive systems, Artificial neural networks, Biological neural networks, Brain modeling, compositionality, data classification, Enhanced fuzzy min–max (EFMM) model, Euclidian geometry, expandability, fuzzy min-max network, fuzzy min–max (FMM) neural network, fuzzy neural nets, fuzzy set theory, histopathological images, hyperbox classifier, hyperbox semiperimeter, IFMM network, image classification, improved FMM network, improved fuzzy min-max network, k-nearest FMM, minimax techniques, nearest neighbour methods, pattern classification, pattern classification problems, pubcrawl, Resiliency, semiperimeter, Training |
| Abstract | Hyperbox classifier is an efficient tool for modern pattern classification problems due to its transparency and rigorous use of Euclidian geometry. Fuzzy min-max (FMM) network efficiently implements the hyperbox classifier, and has been modified several times to yield better classification accuracy. However, the obtained accuracy is not up to the mark. Therefore, in this paper, a new improved FMM (IFMM) network is proposed to increase the accuracy rate. In the proposed IFMM network, a modified constraint is employed to check the expandability of a hyperbox. It also uses semiperimeter of the hyperbox along with k-nearest mechanism to select the expandable hyperbox. In the proposed IFMM, the contraction rules of conventional FMM and enhanced FMM (EFMM) are also modified using semiperimeter of a hyperbox in order to balance the size of both overlapped hyperboxes. Experimental results show that the proposed IFMM network outperforms the FMM, k-nearest FMM, and EFMM by yielding more accuracy rate with less number of hyperboxes. The proposed methods are also applied to histopathological images to know the best magnification factor for classification. |
| DOI | 10.1109/TFUZZ.2019.2924396 |
| Citation Key | kumar_improved_2020 |
An Improved Fuzzy Min–Max Neural Network for Data Classification