Power Distribution Fault Cause Identification With Imbalanced Data Using the Data Mining-Based Fuzzy Classification $E$-Algorithm

Xu, Le; Chow, Mo-Yuen; Taylor, L.S.

doi:10.1109/tpwrs.2006.888990

Cited by 114 publications

(53 citation statements)

References 20 publications

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“…Among others, imbalanced datasets typically appear in the identification of anomalies in certain systems designed to work under steady conditions. Some examples are: Water Distribution Systems [116], the detection of oil spills from satellite images [136], the identification of power distribution fault causes [231] and the prediction of pre-term births [95]. This issue is growing in importance since it appears more and more in most real domains, especially in systems where data from the usual scenario are abundant while data from abnormal ones are scarce.…”

Section: Transformations Required To Fit Technical Assumptions Of Datmentioning

confidence: 99%

A survey on pre-processing techniques: Relevant issues in the context of environmental data mining

Gibert

Sànchez–Marrè

Izquierdo

2016

AIC

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One of the important issues related with all types of data analysis, either statistical data analysis, machine learning, data mining, data science or whatever form of data-driven modeling, is data quality. The more complex the reality to be analyzed is, the higher the risk of getting low quality data. Unfortunately real data often contain noise, uncertainty, errors, redundancies or even irrelevant information. Useless models will be obtained when built over incorrect or incomplete data. As a consequence, the quality of decisions made over these models, also depends on data quality. This is why pre-processing is one of the most critical steps of data analysis in any of its forms. However, pre-processing has not been properly systematized yet, and little research is focused on this. In this paper a survey on most popular pre-processing steps required in environmental data analysis is presented, together with a proposal to systematize it. Rather than providing technical details on specific pre-processing techniques, the paper focus on providing general ideas to a non-expert user, who, after reading them, can decide which one is the more suitable technique required to solve his/her problem.

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Section: Transformations Required To Fit Technical Assumptions Of Datmentioning

confidence: 99%

A survey on pre-processing techniques: Relevant issues in the context of environmental data mining

Gibert

Sànchez–Marrè

Izquierdo

2016

AIC

View full text Add to dashboard Cite

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“…For using a GFS with an imbalanced dataset, the standard fitness function has to be altered, or else changes must be effected on the dataset for raising the importance of misclassifying instances of the minority class. Both techniques have been well studied in the context of GFSs: there are works that deal with the use of costs in fuzzy classifiers for imbalanced datasets 14,44,47,48,52 , and other studies suggest employing a preprocessing step in order to balance the training data before the training 3,19,20,21,22 . In this last respect, we can highlight the re-sampling procedure named "Synthetic Minority Oversampling Technique" or SMOTE 7 ; it has been shown that SMOTE is one the most efficient preprocessing algorithms for imbalanced data in relation with Fuzzy Rule Based Systems (FRBS) 19 .…”

Section: Introductionmentioning

confidence: 99%

Equalizing imbalanced imprecise datasets for genetic fuzzy classifiers

Sánchez¹,

Couso²

2012

IJCIS

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Determining whether an imprecise dataset is imbalanced is not immediate. The vagueness in the data causes that the prior probabilities of the classes are not precisely known, and therefore the degree of imbalance can also be uncertain. In this paper we propose suitable extensions of different resampling algorithms that can be applied to interval valued, multi-labelled data. By means of these extended preprocessing algorithms, certain classification systems designed for minimizing the fraction of misclassifications are able to produce knowledge bases that are also adequate under common metrics for imbalanced classification.

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“…They also provide a stark contrast to the WBCD results, which tend to increase in accuracy when more attributes are used, despite some attributes being poorer. Table 10 shows the calculated g-mean values for animal, tree and lightning-caused faults when the same data is classified using the Naive Bayes approach and the algorithms created by Xu et al The latter consist of an Artificial Neural Network (ANN) [20], an Artificial Immune Recognition System (AIRS) [18], a fuzzy algorithm called the EAlgorithm [19] and a Fuzzy Artificial Immune Recognition System (FAIRS) [25]). FAIRS is a hybrid system that incorporates the quick searching capability and memory mechanism of the AIRS algorithm and the inference rule extracting capability of the E-algorithm.…”

Section: Experimental Results Using the Duke Outage Datasetmentioning

confidence: 99%

Data classification using the Dempster–Shafer method

Chen

Whitbrook

Aickelin

et al. 2014

Journal of Experimental & Theoretical Artificial Intelligen

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In this paper, the Dempster-Shafer method is employed as the theoretical basis for creating data classification systems. Testing is carried out using three popular (multiple attribute) benchmark datasets that have two, three and four classes. In each case, a subset of the available data is used for training to establish thresholds, limits or likelihoods of class membership for each attribute, and hence create mass functions that establish probability of class membership for each attribute of the test data. Classification of each data item is achieved by combination of these probabilities via Dempster's Rule of Combination. Results for the first two datasets show extremely high classification accuracy that is competitive with other popular methods. The third dataset is non-numerical and difficult to classify, but good results can be achieved provided the system and mass functions are designed carefully and the right attributes are chosen for combination. In all cases the Dempster-Shafer method provides comparable performance to other more popular algorithms, but the overhead of generating accurate mass functions increases the complexity with the addition of new attributes. Overall, the results suggest that the D-S approach provides a suitable framework for the design of classification systems and that automating the mass function design and calculation would increase the viability of the algorithm for complex classification problems.

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Power Distribution Fault Cause Identification With Imbalanced Data Using the Data Mining-Based Fuzzy Classification $E$-Algorithm

Cited by 114 publications

References 20 publications

A survey on pre-processing techniques: Relevant issues in the context of environmental data mining

A survey on pre-processing techniques: Relevant issues in the context of environmental data mining

Equalizing imbalanced imprecise datasets for genetic fuzzy classifiers

Data classification using the Dempster–Shafer method

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