Autoencoder network is an efficient representation learning method. In general, a finer
feature set obtained from autoencoder leads to a lower error rate and lower total …

Three-way decisions provide a trisecting-and-acting framework for complex problem solving.
For a cost-sensitive decision-making problem under multiple levels of granularity, sequential …

Abstract Three-way decision (3WD) models have been widely investigated in the fields of
approximate reasoning and decision making. Recently, sequential 3WD models have …

Recently the auto-encoder and its variants have demonstrated their promising results in
extracting effective features. Specifically, its basic idea of encouraging the output to be as …

In the sequential three-way decisions (S3WD) model, classification accuracy is an important
issue. Based on the idea of minimum misclassification, loss functions have been used to …

A fundamental notion of granular computing is a multilevel granular structure in which
different levels are characterized by different degrees of abstraction and details. By making …

Hierarchical classification is a research hotspot in machine learning due to the widespread
existence of data with hierarchical class structures. Existing hierarchical classification …

Auto-Encoders, as one representative deep learning method, has demonstrated to achieve
superior performance in many applications. Hence, it is drawing more and more attentions …

Sequential three-way decision (S3WD) regarded as a dynamic multi-stage decision-making
model, includes three critical components: evaluation function, threshold pair, and …

The theory of sequential three-way decisions which is in accordance with human cognition
is a multi-stage decision-making progress. In fact, sequential strategy can deal with multi …