Fully & Partially-Transmitted-Rule Fusion: A Novel Hierarchical Fuzzy Classification with Application to Nasopharyngeal Cancer’s Metastasis Prediction

Distant metastasis (DM) as a major cause of treatment failure of nasopharyngeal cancer (NPC) actually occurs with a considerably gradual development in the early stage. Therefore, an ideal DM prediction model should be an efficient and interpretable model and simultaneously reflect/simulate this characteristic during its training. Towards such a goal, this study proposes a hierarchical Takagi-Sugeno-Kang (TSK) fuzzy classifier (H-TSKFC) to assure both enhanced classification performance and diversified generation of interpretable fuzzy rules therein through full-partial-rule-transmission fusion for simulating gradual development of DM. Profiting from full-partial-rule-transmission fusion between sub-classifiers, H-TSKFC was endowed with the following benefits. Firstly, a novel stacking mechanism without any use of residuals between sub-classifiers enhances its generalization capability. Secondly, the generation of interpretable fuzzy rules from the second TSK fuzzy sub-classifier provides a diversified way. That is, its useful rules fusion transmitted fully or partially from previous sub-classifier guarantees considerable consistency between sub-classifiers, while its remaining rules reflect gradual difference between them. In this way, the H-TSKFC’s structure naturally mimics the gradual development of DM. Finally, each sub-classifier therein can be trained sequentially and quickly with an analytical solution to accomplish an individual prediction on the original inputs and outputs. Experimental results indeed demonstrate that H-TSKFC possesses linguistic interpretability, along with considerable classification and generalization performance.