An efficient rough feature selection algorithm with a multi-granulation view

This paper focuses on the hybridization technology using rough sets concepts and neural computing for decision and classification purposes Based on the rough set properties, the lower region and boundary region are defined to partition the input signal to a consistent (predictable) part and an inconsistent (random) part In this way, the neural network is designed to deal only with the boundary region, which mainly consists of an inconsistent part of applied input signal causing inaccurate modeling of the data set Owing to different characteristics of neural network (NN) applications, the same structure of conventional NN might not give the optimal solution Based on the knowledge of application in this paper, a block-based neural network (BBNN) is selected as a suitable classifier due to its ability to evolve internal structures and adaptability in dynamic environments This architecture will systematically incorporate the characteristics of application to the structure of hybrid rough-block-based neural network (R-BBNN) A global training algorithm, hybrid particle swarm optimization with wavelet mutation is introduced for parameter optimization of proposed R-BBNN The performance of the proposed R-BBNN algorithm was evaluated by an application to the field of medical diagnosis using real hypoglycemia episodes in patients with Type 1 diabetes mellitus The performance of the proposed hybrid system has been compared with some of the existing neural networks The comparison results indicated that the proposed method has improved classification performance and results in early convergence of the network

Evolvable rough-block-based neural network and its biomedical application to hypoglycemia detection system

In an electronic nose, the most important component is the sensor array and the classification accuracy of an electronic nose that depends significantly upon the choice of the sensors in the array. While deploying an electronic nose for a specific application, it is observed that some of the sensors in the array may not be required and only a subset of the sensor array contributes to the decision. Thus, the number of sensors used in the electronic nose may be minimized for a particular application without affecting the classification accuracy. In many cases, the sensor array produces an imprecise, incomplete, redundant, and inconsistent dataset and thus the classification accuracy degrades due to these redundant sensors. The rough set theory is a mathematical tool capable of selecting the most relevant and nonredundant feature from such datasets. In this paper, the notion of rough set theory is utilized for pattern classification in an electronic nose with black tea samples and at the same time optimization of the sensor set is carried out.

Optimization of Sensor Array in Electronic Nose: A Rough Set-Based Approach

In online transactions of textile products, fabric hand was thought to be inaccessible to consumers. Recently, much effort has been made to study the feasibility of providing consumers with a real sense of fabric through a virtual experience. The current paper proposes to extract fabric hand information from the perspective of visual perception. Two sensory experiments are conducted according to the standardized sensory evaluation procedures on a set of representative textile fabrics by two trained panels. The first experiment is aimed to measure how much fabric hand can be perceived through fabrics’ visual displays. On the basis of the positive results obtained, the second experiment is carried out to further investigate the interactive mechanism between samples’ visual features and their tactile properties. A novel algorithm based on rough set theory and fuzzy set theory is proposed in order to quantitatively measure relations between different sensory information.

Extracting fabric hand information from visual representations of flared skirts

Sparse representations based attribute learning for flower classification

Similar to data mining, three major web mining operations include clustering, association rule mining, and sequential analysis. Typical clustering operations in web mining involve finding natural groupings of web resources or web users. Researchers have found and pointed at some important and fundamental differences between clustering in conventional applications and clustering in web mining. Moreover, due to variety of reasons inherent in web browsing and web logging, the likelihood of bad and incomplete data is higher. This is where Rough Set Theory can play a crucial role and researchers have been utilizing this in clustering the incomplete data and thus aiding in decision making. This paper aims at understanding the Rough Set Theory and its applications in web mining.

Rough Set Theory and Its Applications

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Rough Set Theory and Its Applications