A group incremental feature selection for classification using rough set theory based genetic algorithm

Das, Asit Kumar; Sengupta, Shampa; Bhattacharyya, Siddhartha

doi:10.1016/j.asoc.2018.01.040

Cited by 125 publications

(40 citation statements)

References 24 publications

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“…The mutation is used to maintain genetic diversity and to avoid premature convergence (Das, Sengupta, & Bhattacharyya, ). The process of mutation is flipping one or more gene according to a defined mutation rate.…”

Section: Proposed Methodologymentioning

confidence: 99%

Classification of stomach infections: A paradigm of convolutional neural network along with classical features fusion and selection

Majid

Khan

Yasmin

et al. 2020

Microscopy Res & Technique

121

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Automated detection and classification of gastric infections (i.e., ulcer, polyp, esophagitis, and bleeding) through wireless capsule endoscopy (WCE) is still a key challenge. Doctors can identify these endoscopic diseases by using the computer-aided diagnostic (CAD) systems. In this article, a new fully automated system is proposed for the recognition of gastric infections through multi-type features extraction, fusion, and robust features selection. Five key steps are performed-database creation, handcrafted and convolutional neural network (CNN) deep features extraction, a fusion of extracted features, selection of best features using a genetic algorithm (GA), and recognition. In the features extraction step, discrete cosine transform, discrete wavelet transform strong color feature, and VGG16-based CNN features are extracted. Later, these features are fused by simple array concatenation and GA is performed through which best features are selected based on K-Nearest Neighbor fitness function. In the last, best selected features are provided to Ensemble classifier for recognition of gastric diseases. A database is prepared using four datasets-Kvasir, CVC-ClinicDB, Private, and ETIS-LaribPolypDB with four types of gastric infections such as ulcer, polyp, esophagitis, and bleeding. Using this database, proposed technique performs better as compared to existing methods and achieves an accuracy of 96.5%. K E Y W O R D S

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Section: Proposed Methodologymentioning

confidence: 99%

Classification of stomach infections: A paradigm of convolutional neural network along with classical features fusion and selection

Majid

Khan

Yasmin

et al. 2020

Microscopy Res & Technique

121

View full text Add to dashboard Cite

show abstract

“…Step 1: Information system and knowledge reduction Let S = (U, A, V, f) is an information system, where U represents a non-empty finite set of objects, U= {X 1 , X 2 , ..., X n } called a domain [16]. A represents a non-empty finite set of attributes, A= {A 1 , A 2 , A m }, a j (j ≤ m) is called an attribute; V a is the range of the attribute a; Let us assume that f: U × A → V is an information function for each attribute of each object given a value of information, namely a  A, x  U, f(x, a)  V a .…”

Section: Rough Setmentioning

confidence: 99%

A Data-Driven Condition Monitoring of Product Quality Analysis System Based on RS and AHP

et al. 2020

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Mechanical and electrical products have complex structure and intelligent control system, their reliability plays an important role in the normal operation of security facilities. However, most manufacturers usually pay more attention to the product designing and manufacturing quality, with little interest in the intelligent fault diagnosis. The objective of this study is to develop the products quality intelligent analysis and management system based on Rough Set (RS) and Analytic Hierarchy Process (AHP). Firstly, this paper reviews the principle of hardware, software design, monitoring platform and quality analysis system to reduce the number of information transfer with computer technology. Secondly, the fault types and feature extractions of different faults of elevators are presented and simplified by using RS theory. Then, the objective weight of level index model can be obtained by AHP method, and the comprehensive analysis weight of each index is obtained by using the value of subjective and objective weight coefficients with the golden ratio. Finally, a comprehensive decision weight of the major index for quality control analysis system of many vertical elevators is presented. The results show that the data-driven condition monitoring and quality analysis system is a kind of important means to prevent a disaster of complex mechanical and electrical products.

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“…Among them, rough set theory is a soft computing technique proposed by Z. Pawlak for handling vague, inconsistent and uncertain data (Das et al, 2018). The main idea is to remove redundant or unimportant attributes according to specific rules on the premise of keeping the classification ability of knowledge base unchanged (Wu and Tang, 2019).…”

Section: Designing An Indicator System Of Social Vulnerabilitymentioning

confidence: 99%

Temporal variability of social vulnerability to storm surges in Shenzhen, China

Shen

Kelly³

et al. 2019

Preprint

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Abstract. A social vulnerability evaluation of storm surges is important for any coastal city to commence in order to provide marine disaster preparedness and mitigation procedures and to formulate post-disaster emergency plans for coastal communities. This study establishes an integrated evaluation system of social vulnerability by blending a variety of single evaluation methods, applying the idea of combination weighting and calculating the social vulnerability index of storm surges. Shenzhen, with a current reputation of having the most economic development potential and a representative city in China, is chosen to evaluate its social vulnerability to storm surges via a historical social and economic statistical dataset spanning from 1986 to 2016. The research extends further by analyzing the city’s temporal variability. Results reveal that social vulnerability keeps almost constant from 1986–1991 and 1993–2004, while it decreased sharply in the remainder of times to show a ‘stair-type’ declining curve over the past 30 years. Resilience is progressively increasing by virtue of a continuous increase in medical institutions, fixed asset investments and salary levels of employees. These determinants contribute to the overall downward trend of social vulnerability for Shenzhen. Exposure and sensitivity increased slowly with some fluctuation, causing the changes of social responsibility to transpire.

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A group incremental feature selection for classification using rough set theory based genetic algorithm

Cited by 125 publications

References 24 publications

Classification of stomach infections: A paradigm of convolutional neural network along with classical features fusion and selection

Classification of stomach infections: A paradigm of convolutional neural network along with classical features fusion and selection

A Data-Driven Condition Monitoring of Product Quality Analysis System Based on RS and AHP

Temporal variability of social vulnerability to storm surges in Shenzhen, China

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