In recent years, researchers have paid more and more attention on data mining of practical applications. Aimed to the problem of symptom classification of Chinese traditional medicine, this paper proposes a novel computing model based on the similarities among attributes of high dimension data to compute the similarity between any tuples. This model assumes data attributes as basic vectors of m...

Attribute reduction is one of the most important problems in rough set theory. This paper deals with attribute reduction in tolerance information systems based on Dempster-shafter theory of evidence. The concepts of plausibility consistent set and belief consistent set are introduced in tolerance information systems. Furthermore, relative plausibility reduction and belief reduction are discusse...

In this paper, we introduce an implementation of the attribute selection algorithm, Correlation-based Feature Selection (CFS) integrated with our k-nearest neighbour (k-NN) framework. Binary neural networks underpin our k-NN and allow us to create a unified framework for attribute selection, prediction and classification. We apply the framework to a real world application of predicting bus jour...

The rough set theory provides a formal framework for data mining. Reduct is the most important concept in rough set application to data mining. A reduct is the minimal attribute set preserving classification power of original dataset. Finding a reduct is similar to feature selection problem. In this paper, we propose two reduct algorithms. One is based on attribute frequency in discernibility m...

Generalized zero-shot learning (GZSL) is a technique to train deep model identify unseen classes using the attribute. In this paper, we put forth new GZSL that improves classification performance greatly. Key idea of proposed approach, henceforth referred as semantic feature extraction-based (SE-GZSL), use containing only attribute-related information in relationship between image and doing so,...

Rough set theory provides a systematic way for rule extraction, attribute reduction and knowledge classification in information systems. Some measurements are important in rough sets. For example, information entropy, knowledge dependency are useful in attribute reduction algorithms. This paper proposes the concepts of the lower and upper covering numbers to establish measurements in covering-b...

In the paper, two families of lazy classification algorithms of polynomial time complexity are considered. These algorithms are based on deterministic (with a relation “attribute = value” on the right hand side) and inhibitory (with a relation “attribute 6= value” on the right hand side) rules, but the direct generation of rules is not required. Instead of this, the considered algorithms extrac...

We address zero-shot (ZS) learning, building upon prior work in hierarchical classification by combining it with approaches based on semantic attribute estimation. For both non-novel and novel image classes we compare multiple formulations of the problem, starting with deep universal features in each case. We investigate the effect of using different posterior probabilities as inputs to the hie...

The goal of minimal attribute reduction is to find the minimal subset R of the condition attribute set C such that R has the same classification quality as C. This problem is well known to be NP-hard. When only one minimal attribute reduction is required, it was transformed into a nonlinearly constrained combinatorial optimization problem over a Boolean space and some heuristic search approache...