Contrast-Set Mining of Aircraft Accidents and Incidents

Nazeri, Zohreh; Barbará, Daniel; Jong, Kenneth De; Donohue, George; Sherry, Lance

doi:10.1007/978-3-540-70720-2_24

Cited by 12 publications

(7 citation statements)

References 4 publications

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“…Satsangi and Zaiane (2007) use the association rule-based approach to find contrast sets. At the same time, STUCCO has been used to address real-world problems in many fields, such as education (Minaei-Bidgoli et al 2004), medical research (Kralj et al 2007a, b;Siu et al 2005), and the aviation industry (Nazeri et al 2008). Aside from the research extending contrast sets to a more specific format, there is other research that looks at finding interesting contrast sets among all discovered contrast sets (Loekito and Bailey 2008).…”

Section: Related Workmentioning

confidence: 97%

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A Tree-Based Contrast Set-Mining Approach to Detecting Group Differences

Liu

Yang

Chen

et al. 2014

INFORMS Journal on Computing

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U nderstanding differences between groups in a data set is one of the fundamental tasks in data analysis.As relevant applications accumulate, data-mining methods have been developed to specifically address the problem of group difference detection. Contrast set mining discovers group differences in the form of conjunction of feature-value pairs or items. In this paper, we incorporate absolute difference, relative difference, and statistical significance in our definition of a group difference, and develop a novel method named DIFF that uses the prefix-tree structure to compress the search space, follows a tree traversal procedure to discover the complete set of significant group differences, and employs efficient pruning strategies to expedite the search process. We conducted comprehensive experiments to compare our method with existing methods on completeness of results, pruning efficiency, and computational efficiency. The experiments demonstrate that our method guarantees completeness of results and achieves higher pruning efficiency and computational efficiency compared to STUCCO. In addition, our definition of group difference is more general than STUCCO. Our method is more effective than traditional approaches, such as classification trees, in discovering the complete set of significant group differences.

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Section: Related Workmentioning

confidence: 97%

“…For example, Nazeri et al (2008) discovered differences between aircrafts that had been involved in accidents and those that had not. They used these differences to uncover several important causes of airplane accidents.…”

Section: Introductionmentioning

confidence: 96%

A Tree-Based Contrast Set-Mining Approach to Detecting Group Differences

Liu

Yang

Chen

et al. 2014

INFORMS Journal on Computing

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“…The rate-making formulas for property and casualty insurance illustrate that to determine the basic rate, insurers need to evaluate every underwriting class's overall loss experience (18). Past losses (occurrence and severity) have always been regarded as the most direct and reliable indicator of the insured's risk level and could also reflect the short-term trend (57). Traditionally, actuarial fair premiums are determined for the expected loss amount (58), and charging premiums high enough to pay all claims and expenses associated with underwritten risk is one of the most crucial pricing objectives (18).…”

Section: The Dimensions and Factors Of Pricing Gaa Insurance Premium ...mentioning

confidence: 99%

Aircraft Insurance Costs Management for Sustainable General Aviation: Insights From General Aviation Enterprises in China

Wang

2022

Transportation Research Record: Journal of the Transportation R

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Little research has examined how to effectively reduce general aviation aircraft (GAA) insurance costs (premiums), even though it has grown to be a heavy financial burden adversely affecting the sustainable development of general aviation (GA). In contrast to previous research that dealt solely with airline insurance, this research studies the insurance cost management issue of GAA from the perspective of supply and demand. Specifically, this paper focuses on analyzing significant factors influencing GAA insurance premiums and the degree of incidence with the risk system. This empirical research is based on a unique dataset of GA enterprises in China during 2017 to 2019 and utilizes the second synthetic of gray incidence analysis (SSDGIA) model and entropy weight analysis. First, it is found that the fleet profile, loss record, and individual GA operational performance are the critical elements related to premiums. Additionally, there is an increasing dependence of premiums on direct loss performance. Second, the degree of incidence between premiums and the risk system (<0.8) constructed within the Human Factors Analysis and Classification System (HFACS) framework is below the expected level. The latent risk factors related to loss make a small contribution. Such findings will contribute to GA development through cost reduction and risk management. GAA operators and owners could save premium expenses by optimizing their risk management decisions and operations. The results also provide an important theoretical basis for the regulatory agency to speed up the optimization of GA risk information analysis and disclosure system. With complete GA risk information support, insurers could further improve GA risk assessment and determine long-term competitive premium rates.

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“…While the majority of contrast set research concerned medical data [12,13,14,22], the different areas of application like aircraft incidents [23], software crashes [24], or folk music [25] were also investigated in the literature.…”

Section: Related Workmentioning

confidence: 99%

Separate and conquer heuristic allows robust mining of contrast sets from various types of data

Gudyś¹,

Sikora²,

Wróbel³

2022

Preprint

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Identifying differences between groups is one of the most important knowledge discovery problems. The procedure, also known as contrast sets mining, is applied in a wide range of areas like medicine, industry, or economics.In the paper we present RuleKit-CS, an algorithm for contrast set mining based on a sequential covering -a well established heuristic for decision rule induction. Multiple passes accompanied with an attribute penalization scheme allow generating contrast sets describing same examples with different attributes, unlike the standard sequential covering. The ability to identify contrast sets in regression and survival data sets, the feature not provided by the existing algorithms, further extends the usability of RuleKit-CS.Experiments on wide range of data sets confirmed RuleKit-CS to be a useful tool for discovering differences between defined groups. The algorithm is a part of the RuleKit suite available at GitHub under GNU AGPL 3 licence (https://github.com/adaa-polsl/RuleKit).

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Contrast-Set Mining of Aircraft Accidents and Incidents

Cited by 12 publications

References 4 publications

A Tree-Based Contrast Set-Mining Approach to Detecting Group Differences

A Tree-Based Contrast Set-Mining Approach to Detecting Group Differences

Aircraft Insurance Costs Management for Sustainable General Aviation: Insights From General Aviation Enterprises in China

Separate and conquer heuristic allows robust mining of contrast sets from various types of data

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