Outlier detection based on granular computing and rough set theory

Jiang, Feng

doi:10.1007/s10489-014-0591-4

Cited by 49 publications

(6 citation statements)

References 55 publications

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“…In the last three decades, RST has been attracted many researchers in many various fields. Such as Multi-source alert data understanding [15], vertex cover problem [16], Self-adaptive Extreme Learning Machine [17], Group multi-criteria design concept evaluation [18], Thyroid disease diagnosis [19], Road Safety Indicator Analysis [20], Low carbon technology integration innovation assessment [21], Outlier detection [22], Integrating agricultural sustainability into policy planning [23]. However, numerical attributes and hybrid attributes may both be used in practice.…”

Section: Attribute Reduction Based On Fuzzy Rough Setmentioning

confidence: 99%

City traffic flow breakdown prediction based on fuzzy rough set

Yang

et al. 2017

Open Physics

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Abstract:In city traffic management, traffic breakdown is a very important issue, which is defined as a speed drop of a certain amount within a dense traffic situation. In order to predict city traffic flow breakdown accurately, in this paper, we propose a novel city traffic flow breakdown prediction algorithm based on fuzzy rough set. Firstly, we illustrate the city traffic flow breakdown problem, in which three definitions are given, that is, 1) Pre-breakdown flow rate, 2) Rate, density, and speed of the traffic flow breakdown, and 3) Duration of the traffic flow breakdown. Moreover, we define a hazard function to represent the probability of the breakdown ending at a given time point. Secondly, as there are many redundant and irrelevant attributes in city flow breakdown prediction, we propose an attribute reduction algorithm using the fuzzy rough set. Thirdly, we discuss how to predict the city traffic flow breakdown based on attribute reduction and SVM classifier. Finally, experiments are conducted by collecting data from I-405 Freeway, which is located at Irvine, California. Experimental results demonstrate that the proposed algorithm is able to achieve lower average error rate of city traffic flow breakdown prediction.

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Section: Attribute Reduction Based On Fuzzy Rough Setmentioning

confidence: 99%

City traffic flow breakdown prediction based on fuzzy rough set

Yang

et al. 2017

Open Physics

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“…Analysis and detection of outlier and spurious data are investigated in several works by Jiang et al (2005), Nyuyen (2008), Shaari et al (2009), Chen et al (2010) and Jiang and Chen (2015). A commonality among these works on outlier detection is the adoption of Pawlak theory of rough sets and its capability of approximating sets, via lower and upper approximation functions, to detect outlier objects having abnormal attributes and properties (generally in boundary regions).…”

Section: Perceptionmentioning

confidence: 99%

“…A commonality among these works on outlier detection is the adoption of Pawlak theory of rough sets and its capability of approximating sets, via lower and upper approximation functions, to detect outlier objects having abnormal attributes and properties (generally in boundary regions). In the work by Shaari et al (2009) is used the concept of Non-Reduct to discover a set of attributes that may contain outliers, Chen et al (2010) proposes the adoption of outlier detection algorithm based on the neighbourhood rough set model, Jiang and Chen (2015) introduce the concept of GR-based outliers and proposes a detection algorithm working on this concept. With regards to spatiotemporal requirements, a specific application for detecting spatial and temporal outliers is proposed in Albanese et al (2014).…”

Section: Perceptionmentioning

confidence: 99%

Enforcing situation awareness with granular computing: a systematic overview and new perspectives

et al. 2016

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Situation Awareness is defined by Endsley as ''the perception of the elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future'' and it deals with the continuous extraction of environmental information and its integration with prior knowledge for directing further perception and anticipating future events. To realize systems for Situation Awareness, individual pieces of raw information (e.g. sensor data) should be interpreted into a higher, domain-relevant concept called ''situation'', which is an abstract state of affairs interesting to specific applications. The power of using ''situations'' lies in their ability to provide a simple, human-understandable representation of, for instance, sensor data. The aim of this work is to propose an overview of the applications of Computational Intelligence and Granular Computing for the implementation of systems supporting Situation Awareness. In this scenario, several and heterogeneous Computational Intelligence models and techniques (e.g. Fuzzy Cognitive Maps, Fuzzy Formal Concept Analysis, Dempster-Shafer Theory of Evidence, Ontologies, Knowledge Reasoning, Evolutionary Computing, Intelligent Agents) can be employed to implement such systems. Moreover, in a Situation Identification process, huge volumes of heterogeneous data need processing (e.g. fusion). With respect to this issue, Granular Computing is an information processing theory for using ''granules'' (e.g. subsets, intervals, fuzzy sets) effectively to build an efficient computational model for dealing with the above-mentioned data. The overview is proposed coherently to both methodological and architectural viewpoints for Situation Awareness.

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“…In addition, methods based on statistical calculations are applied, in particular they are Gaussian mixture model or hidden Markov model [19]- [23]. Recently, fuzzy computing models have appeared in conjunction with the available anomaly detection techniques [24]. It is worth to highlight fuzzy logic [25], [26], fuzzy cluster analysis techniques including fuzzy c-means and fuzzy c-medoids [27], [28], fuzzy dynamic Markov model [29], and many others.…”

Section: Introductionmentioning

confidence: 99%

Isolation Forest Based on Minimal Spanning Tree

2022

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Detecting anomalies in data sets has been one of the most studied issues in modern data analysis. Therefore, there is a plethora of applications in a very wide range of fields of science and technology. One of the most frequently used anomaly detection methods is Isolation Forest. In this study, we propose a novel efficient approach based on this technique. In order to improve the classification accuracy of the base method, we make two-fold modifications. First, we propose a change of the technique of building isolation trees to merge nodes by minimal spanning tree algorithm. The second change is based on a modification of the function assessing the anomaly of the analyzed element (data record) to sum of factors correlated with tree height and nearest point distance. In the series of comprehensive computational experiments, the proposed method has proven to produce better results than other compared state-of-the-art methods available in popular data mining programming libraries. It is worth stressing that the final version of the new method in comparison to original Isolation Forest is 2.9% better in terms of AUC measure.

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Outlier detection based on granular computing and rough set theory

Cited by 49 publications

References 55 publications

City traffic flow breakdown prediction based on fuzzy rough set

City traffic flow breakdown prediction based on fuzzy rough set

Enforcing situation awareness with granular computing: a systematic overview and new perspectives

Isolation Forest Based on Minimal Spanning Tree

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