Outlier Detection Based on Fuzzy Rough Granules in Mixed Attribute Data

Yuan, Zhong; Chen, Hongmei; Li, Tianrui; Sang, Binbin; Wang, Shu

doi:10.1109/tcyb.2021.3058780

Cited by 38 publications

(19 citation statements)

References 44 publications

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“…(4) Yuan et al [38] introduced fuzzy rough sets to deal with the problem of outlier detection in hybrid data (numerical, categorical). First,t h e y defined the granule outlier degree to characterize the outlier degree of fuzzy rough granules by employing the fuzzy approximation accuracy.…”

Section: Discussion and Contributionmentioning

confidence: 99%

Measures of uncertainty for a four-hybrid information system and their applications

2022

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A four-hybrid information system (4HIS) is an information system (IS) where the dataset of object descriptions consists of categorical, boolean, real-valued and missing data or attributes. This paper studies measures of uncertainty for a 4HIS and its application in attribute reduction. The distance function for each type of attribute in a 4HIS is first provided. Then, this distance is used to produce the tolerance relation induced by a given subsystem in a 4HIS. Next, information structure of this subsystem is proposed in terms of a set vector and dependence between information structures is introduced. Moreover, granulation and entropy measures in a 4HIS are investigated on the basis of information structures. In order to verify the feasibility of the proposed measures, effectiveness analysis is performed from a statistical perspective. Finally, an application of the proposed measures for attribute reduction in a 4HIS is given.

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Section: Discussion and Contributionmentioning

confidence: 99%

Measures of uncertainty for a four-hybrid information system and their applications

2022

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“…In this section, experimental analysis of the Random Projection Deep Extreme Learning-based Chebyshev Reflective Correlation (RPDEL-CRC) method for outlier detection in data mining is presented. In this section, the performance of the proposed RPDEL-CRC is compared with the state-of-the-art methods, fuzzy rough granules-based outlier detection (FRGOD) [1] and Iterative ensemble method with distance-based data filtering [2] using NIFT-50 Stock Market Dataset (https://www.kaggle.com/datasets/rohanrao/nifty50stock-market-data). Simulations are performed in R Programming language.…”

Section: Methodsmentioning

confidence: 99%

“…Different types of outlier detection models are said to exist. In order to determine the perpetual temporal outliers, we obtain outliers As shown in the above figure, with the obtained clusters for the given dataset 'DS', in a 'd-dimensional' vector, with data point denoted by 'DP={DP [1],DP [2],…DP[d] }' at time instance 'T', distance between two points '〖DP 〗_i' and 'DP_j' employing Chebyshev distance is mathematically, expressed as given below.…”

Section: Chebyshev Temporal and Reflective Correlation-based Outlier ...mentioning

confidence: 99%

“…Most of the prevailing research works concentrates on outlier detection for categorical or numerical attribute data. A fuzzy rough set (FRSs) was proposed in [1] to detect outlier in mixed attribute data based on fuzzy rough granules. Initially, the granule outlier degree (GOD) was designed with the objective of characterizing the outlier degree of fuzzy rough granules via fuzzy approximation accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Gaussian Projection Deep Extreme Clustering and Chebyshev Reflective Correlation based Outlier Detection

Rajalakshmi¹,

Madhubala²

2022

IJACSA

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Outlier detection or simply the task of point detection that are noticeably distinct and different from data sample is a predominant issue in deep learning. When a framework is constructed, these distinctive points can later lead to model training and compromise accurate predictions. Owing to this reason, it is paramount to recognize and eliminate them before constructing any supervised model and this is frequently the initial step when dealing with a deep learning issue. Over the recent few years, different numbers of outlier detector algorithms have been designed that ensure satisfactory results. However, their main disadvantages remain in the time and space complexity and unsupervised nature. In this work, a clusteringbased outlier detection called, Random Projection Deep Extreme Learning-based Chebyshev Reflective Correlation (RPDEL-CRC) is proposed. First, Gaussian Random Projection-based Deep Extreme Learning-based Clustering model is designed. Here, by applying Gaussian Random Projection function to the Deep Extreme Learning obtains the relevant and robust clusters corresponding to the data points in a significant manner. Next, with the robust clusters, outlier detection time is said to be reduced to a greater extent. In addition, a novel Chebyshev Temporal and Reflective Correlation-based Outlier Detection model is proposed to detect outliers therefore achieving high outlier detection accuracy. The proposed approach is validated with the NIFTY-50 stock market dataset. The performance of the RPDEL-CRC method is evaluated by applying it to NIFTY-50 Stock Market dataset. Finally, we compare the results of the RPDEL-CRC method to the state-of-the-art outlier detection methods using outlier detection time, accuracy, error rate and false positive rate evaluation metrics.

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“…The construction of granules using equivalence relations is a global granulation method, which has a high time and space complexity [33,34]. To solve this problem, the mean samples of each class are introduced to construct the optimal samples.…”

Section: Introductionmentioning

confidence: 99%

PGNN: A Parallel Granular BP Neural Network for Classification

Hou

Chen

et al. 2022

Preprint

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Deep learning models based on BP neural networks are widely used in classification and regression problems. However, they have a wide range of parameters and insufficient local and uncertain information representations. Granular computing has great uncertainty characterization capability and allows input patterns to be abstracted at higher levels. A parallel granular BP neural network is proposed by combining clustered granulation to reconfigure the BP neural network. First, the optimal samples are defined and used as the references for granulating. Features are granulated into granules. And samples are granulated into granular vectors. Secondly, the granule vector is input to the granule BP neural network, and the granules for classification are output after the granule BP forward calculation, and are reduced to the original classification results by the threshing process. The granular BP neural network defines granular activation functions and a granular loss function to fit the input pattern of granular vectors while retaining the original fully connected neural network structure. The problem to be processed is extended into granules by granulation, and the structured characteristics of the granules make the granular BP neural network computable in parallel. Finally, compared with DNN, SVM, DT and NB, PGNN has better classification accuracy and improved computational efficiency and enhanced generalization performance.

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Outlier Detection Based on Fuzzy Rough Granules in Mixed Attribute Data

Cited by 38 publications

References 44 publications

Measures of uncertainty for a four-hybrid information system and their applications

Measures of uncertainty for a four-hybrid information system and their applications

Gaussian Projection Deep Extreme Clustering and Chebyshev Reflective Correlation based Outlier Detection

PGNN: A Parallel Granular BP Neural Network for Classification

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