Research on Information Technology with Character Pattern Recognition Method Based on Rough Set Theory

Liu, Yong Li

doi:10.4028/www.scientific.net/amr.886.519

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…The true perception of the judges can be apperceived more deeply with RS method. So it is widely applied in fault diagnosis [ 31 – 32 ], prediction and control [ 33 – 34 ], pattern recognition [ 35 – 36 ] and data mining [ 37 – 39 ] etc. If the indicator system has a large number of attributes and complex relationships, using RS method to determine the weight is often with a heavy calculation workload and low feasibility.…”

Section: Introductionmentioning

confidence: 99%

Production Task Queue Optimization Based on Multi-Attribute Evaluation for Complex Product Assembly Workshop

2015

PLoS ONE

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The production task queue has a great significance for manufacturing resource allocation and scheduling decision. Man-made qualitative queue optimization method has a poor effect and makes the application difficult. A production task queue optimization method is proposed based on multi-attribute evaluation. According to the task attributes, the hierarchical multi-attribute model is established and the indicator quantization methods are given. To calculate the objective indicator weight, criteria importance through intercriteria correlation (CRITIC) is selected from three usual methods. To calculate the subjective indicator weight, BP neural network is used to determine the judge importance degree, and then the trapezoid fuzzy scale-rough AHP considering the judge importance degree is put forward. The balanced weight, which integrates the objective weight and the subjective weight, is calculated base on multi-weight contribution balance model. The technique for order preference by similarity to an ideal solution (TOPSIS) improved by replacing Euclidean distance with relative entropy distance is used to sequence the tasks and optimize the queue by the weighted indicator value. A case study is given to illustrate its correctness and feasibility.

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Section: Introductionmentioning

confidence: 99%

Production Task Queue Optimization Based on Multi-Attribute Evaluation for Complex Product Assembly Workshop

2015

PLoS ONE

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Implementing algorithms of rough set theory and fuzzy rough set theory in the R package “RoughSets”

Riza

Janusz

Bergmeir

et al. 2014

Information Sciences

150

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Outlier detection using conditional information entropy and rough set theory

Li,

Wei,

Liu

2024

IFS

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Outlier detection is critically important in the field of data mining. Real-world data have the impreciseness and ambiguity which can be handled by means of rough set theory. Information entropy is an effective way to measure the uncertainty in an information system. Most outlier detection methods may be called unsupervised outlier detection because they are only dealt with unlabeled data. When sufficient labeled data are available, these methods are used in a decision information system, which means that the decision attribute is discarded. Thus, these methods maybe not right for outlier detection in a a decision information system. This paper proposes supervised outlier detection using conditional information entropy and rough set theory. Firstly, conditional information entropy in a decision information system based on rough set theory is calculated, which provides a more comprehensive measure of uncertainty. Then, the relative entropy and relative cardinality are put forward. Next, the degree of outlierness and weight function are presented to find outlier factors. Finally, a conditional information entropy-based outlier detection algorithm is given. The performance of the given algorithm is evaluated and compared with the existing outlier detection algorithms such as LOF, KNN, Forest, SVM, IE, and ECOD. Twelve data sets have been taken from UCI to prove its efficiency and performance. For example, the AUC value of CIE algorithm in the Hayes data set is 0.949, and the AUC values of LOF, KNN, SVM, Forest, IE and ECOD algorithms in the Hayes data set are 0.647, 0.572, 0.680, 0.676, 0.928 and 0.667, respectively. The advantage of the proposed outlier detection method is that it fully utilizes the decision information.

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Research on Information Technology with Character Pattern Recognition Method Based on Rough Set Theory

Cited by 3 publications

References 5 publications

Production Task Queue Optimization Based on Multi-Attribute Evaluation for Complex Product Assembly Workshop

Production Task Queue Optimization Based on Multi-Attribute Evaluation for Complex Product Assembly Workshop

Implementing algorithms of rough set theory and fuzzy rough set theory in the R package “RoughSets”

Outlier detection using conditional information entropy and rough set theory

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