A Review on Advanced Decision Trees for Efficient & Effective k-NN Classification

K Nearest Neighbor (KNN) strategy is a notable classification strategy in data mining and estimations in light of its direct execution and colossal arrangement execution. In any case, it is outlandish for ordinary KNN strategies to select settled k esteem to all tests. Past courses of action assign different k esteems to different test tests by the cross endorsement strategy however are typically tedious. This work proposes new KNN strategies, first is a KTree strategy to learn unique k esteems for different test or new cases, by including a training arrange in the KNN classification. This work additionally proposes a change rendition of KTree technique called K*Tree to speed its test organize by putting additional data of the training tests in the leaf node of KTree, for example, the training tests situated in the leaf node, their KNNs, and the closest neighbor of these KNNs. K*Tree, which empowers to lead KNN arrangement utilizing a subset of the training tests in the leaf node instead of all training tests utilized in the recently KNN techniques. This really reduces the cost of test organize

A Robust Multilabel Method Integrating Rule-based Transparent Model, Soft Label Correlation Learning and Label Noise Resistance

Model transparency, label correlation learning and the robust-ness to label noise are crucial for multilabel learning. However, few existing methods study these three characteristics simultaneously. To address this challenge, we propose the robust multilabel Takagi-Sugeno-Kang fuzzy system (R-MLTSK-FS) with three mechanisms. First, we design a soft label learning mechanism to reduce the effect of label noise by explicitly measuring the interactions between labels, which is also the basis of the other two mechanisms. Second, the rule-based TSK FS is used as the base model to efficiently model the inference relationship be-tween features and soft labels in a more transparent way than many existing multilabel models. Third, to further improve the performance of multilabel learning, we build a correlation enhancement learning mechanism based on the soft label space and the fuzzy feature space. Extensive experiments are conducted to demonstrate the superiority of the proposed method.Comment: This paper has been accepted by IEEE Transactions on Fuzzy System