Improved variable and value ranking techniques for mining categorical traffic accident data

Wang, Huanjing; Parrish, Allen; Smith, Randy K.; Vrbsky, Susan V.

doi:10.1016/j.eswa.2005.06.007

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…There are different methods for variable ranking like Chi-Square, correlation coefficient (CC), information gain (IG), and gain ratio (GR). In this study, the GR follows the variable ranking method [38]. The gain ratio is calculated as…”

Section: Feature Selectionmentioning

confidence: 99%

Novel Computer-Aided Diagnosis System for the Early Detection of Alzheimer’s Disease

Alharbi¹,

Ziyad²

2023

Computers, Materials &Amp; Continua

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Aging is a natural process that leads to debility, disease, and dependency. Alzheimer's disease (AD) causes degeneration of the brain cells leading to cognitive decline and memory loss, as well as dependence on others to fulfill basic daily needs. AD is the major cause of dementia. Computer-aided diagnosis (CADx) tools aid medical practitioners in accurately identifying diseases such as AD in patients. This study aimed to develop a CADx tool for the early detection of AD using the Intelligent Water Drop (IWD) algorithm and the Random Forest (RF) classifier. The IWD algorithm an efficient feature selection method, was used to identify the most deterministic features of AD in the dataset. RF is an ensemble method that leverages multiple weak learners to classify a patient's disease as either demented (DN) or cognitively normal (CN). The proposed tool also classifies patients as mild cognitive impairment (MCI) or CN. The dataset on which the performance of the proposed CADx was evaluated was sourced from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The RF ensemble method achieves 100% accuracy in identifying DN patients from CN patients. The classification accuracy for classifying patients as MCI or CN is 92%. This study emphasizes the significance of pre-processing prior to classification to improve the classification results of the proposed CADx tool.

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Section: Feature Selectionmentioning

confidence: 99%

Novel Computer-Aided Diagnosis System for the Early Detection of Alzheimer’s Disease

Alharbi¹,

Ziyad²

2023

Computers, Materials &Amp; Continua

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“…Singh [10] et al improved the performance of Apriori algorithm and proposed two improved Apriori algorithms based on MapReduce: VFPC (Elapsed Time-based Dynamic pass-counting) and ETDPC (Elapsed Time-based Dynamic pass-counting). Wang [11] et al discussed the algorithm for sorting variables in the data set and proposed two new technologies, MG and SMGR. These two technologies were developed for public safety using classified traffic crash data, which were more intuitive than existing technologies.…”

Section: Introductionmentioning

confidence: 99%

A Parallel FP-Growth Mining Algorithm with Load Balancing Constraints for Traffic Crash Data

Yang

Tian

Wang

et al. 2022

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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Traffic safety is an important part of the roadway in sustainable development. Freeway traffic crashes typically cause serious casualties and property losses, being a serious threat to public safety. Figuring out the potential correlation between various risk factors and revealing their coupling mechanisms are of effective ways to explore and identity freeway crash causes. However, the existing association rule mining algorithms still have some limitations in both efficiency and accuracy. Based on this consideration, using the freeway traffic crash data obtained from WDOT (Washington Department of Transportation), this research constructed a multi-dimensional multilevel system for traffic crash analysis. Considering the load balancing, the FP-Growth (Frequent Pattern- Growth) algorithm was optimized parallelly based on Hadoop platform, to achieve an efficient and accurate association rule mining calculation for massive amounts of traffic crash data; then, according to the results of the coupling mechanism among the crash precursors, the causes of freeway traffic crashes were identified and revealed. The results show that the parallel FPgrowth algorithm with load balancing constraints has a better operating speed than both the conventional FP-growth algorithm and parallel FP-growth algorithm towards processing big data. This improved algorithm makes full use of Hadoop cluster resources and is more suitable for large traffic crash data sets mining while retaining the original advantages of conventional association rule mining algorithm. In addition, the mining association rules model with the improvement of multi-dimensional interaction proposed in this research can catch the occurrence mechanism of freeway traffic crash with serious consequences (lower support degree probably) accurately and efficiently.

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“… Confianza de una regla R i : mide la frecuencia relativa de los ejemplos que satisfacen antecedente y consecuente entre aquellos que satisfacen sólo el antecedente (H Wang et al 2005). (Webb y Songmao Zhang 2005) la expresan como el cociente entre el soporte y la cobertura:…”

Section: Definición De La Función De Fitnessunclassified

Contribución a la Aplicación de Técnicas de Inteligencia Artificial para el diseño efectivo de Sistemas Adaptativos de Aprendizaje Competitivo

Pérez¹

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Esta tesis doctoral se la dedico a mi familia, especialmente a mis padres porque sin su inestimable ayuda y su apoyo incondicional no hubiera podido realizar este trabajo, y a mis hijas Diana y Elena, que, sin saberlo, han sido mi fuente de energía y mi inyección de voluntad. Además, quería agradecer a mis compañeros y amigos María Jesús, Luisa y Juan Pablo, por su apoyo, consejos, y ayuda durante el desarrollo de esta tesis. Por último, quería también dar las gracias a todos los profesores y compañeros que de una manera u otra me han ayudado en el desarrollo

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Improved variable and value ranking techniques for mining categorical traffic accident data

Cited by 7 publications

References 17 publications

Novel Computer-Aided Diagnosis System for the Early Detection of Alzheimer’s Disease

Novel Computer-Aided Diagnosis System for the Early Detection of Alzheimer’s Disease

A Parallel FP-Growth Mining Algorithm with Load Balancing Constraints for Traffic Crash Data

Contribución a la Aplicación de Técnicas de Inteligencia Artificial para el diseño efectivo de Sistemas Adaptativos de Aprendizaje Competitivo

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