Incorrect attribute value detection for traffic accident data

Deb, Rupam; Liew, Alan Wee‐Chung

doi:10.1109/ijcnn.2015.7280635

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…Deriving meaningful information from the raw data requires pre-processing of data that converts real-time data into a computer-readable format. The pre-processing stage involves attribute selection, data cleaning, and data transformation (Deb and Liew, 2015;Rathi and Acharjya, 2018). This process starts with data collection, and then the required features or attributes have been selected from the raw data, ready for analysis.…”

Section: Data Pre-processingmentioning

confidence: 99%

Customer Classification in Indian Retail Sector- A Comparative Analysis of Various Machine Learning Approaches

Sreekumar¹,

Gopalan²,

Desai³

et al. 2020

ijoqm

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The retail industry across the world is realizing that delivering high levels of service quality and achieving customer satisfaction is the key for a sustainable competitive advantage. Researchers have found positive relations between retail service quality dimensions and customer satisfaction. Identifying and classifying the retail customers as 'satisfied' or 'dissatisfied' according to the retail service quality dimensions would be useful to retailers in enabling strategic decision making in a competitive and dynamic environment. Retailers generate and collect a huge amount of customer data on daily transactions, customer-shopping history, goods transportation, consumption patterns, and service records in a relatively short period. The explosive growth of data requires a more efficient way to extract useful knowledge which can help the retailers to make better business decisions and to target customers who might be profitable to them. The concept of data mining has emerged as an effective technique for exploring large amounts of data to discover meaningful patterns and rules in various fields including retail. In this paper, the retail customers are classified into either 'satisfied' or 'dissatisfied' classes according to the retail service quality dimensions. The research presents a comparative study of popular classification techniques such as decision tree classifier and support vector machine using the R-studio software. The paper uses machine learning algorithms to assess the Indian retail service quality. The results would help the retail organizations to enhance their overall service quality and to target their marketing efforts at the right group of customers.

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Section: Data Pre-processingmentioning

confidence: 99%

Customer Classification in Indian Retail Sector- A Comparative Analysis of Various Machine Learning Approaches

Sreekumar¹,

Gopalan²,

Desai³

et al. 2020

ijoqm

View full text Add to dashboard Cite

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“…Identification of noisy data is an important data preprocessing task for improving data quality. Many noise detection algorithms have been proposed for various applications [2,5,13,16,17,21,23,24]. Among them, HCleaner [21], NOISERANK [16], Polishing method [17] and Error Detection and Impact-sensitive instance Ranking (EDIR) [23] are some wellknown noisy value detection algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…We use four types of noisy patterns in our test datasets: simple, medium, complex, and blended [3,5]. In a simple pattern, a record can have at most one noisy value, whereas in a medium pattern, a record can have noisy values for up to 50% of the attributes.…”

Section: Noisy Values Simulationmentioning

confidence: 99%

Noisy values detection and correction of traffic accident data

Deb

Liew

2019

Information Sciences

Self Cite

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Death, injury, and disability from road traffic crashes continue to be a major global public health problem. Therefore, methods to reduce accident severity are of significant interest to traffic agencies and the public at large. Noisy data in the traffic accident dataset obscure the discovery of important factors and mislead conclusions. Identifying and correcting noisy values is an important goal of data cleansing and preprocessing. This paper proposes a new algorithm called NoiseCleaner to identify and correct noisy categorical attributes values in large traffic accident datasets. We evaluate our algorithm using four publicly available traffic accident datasets from Australia and United States, namely, two road crash datasets from the Queensland Government data depository (data.qld.gov.au) and two datasets from the New York's open data portal (data.ny.gov). We compare our technique with several existing stateof-the-art methods and show that our algorithm performs significantly better than the existing algorithms.

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“…The preprocessing is an important phase in data mining. This stage involves the attribute selection, data cleaning, and data transformation [8]. This process starts off with data collection, then the required features or attributes have been selected from the raw data, ready for analysis.…”

Section: B Data Pre-processingmentioning

confidence: 99%

A Comparative Study of Classification Algorithms using Data Mining: Crime and Accidents in Denver City the USA

Gupta¹,

Syed²,

Mohammad³

et al. 2016

ijacsa

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Abstract-In the last five years, crime and accidents rates have increased in many cities of America. The advancement of new technologies can also lead to criminal misuse. In order to reduce incidents, there is a need to understand and examine emerging patterns of criminal activities. This paper analyzed crime and accident datasets from Denver City, USA during 2011 to 2015 consisting of 372,392 instances of crime. The dataset is analyzed by using a number of Classification Algorithms. The aim of this study is to highlight trends of incidents that will in return help security agencies and police department to discover precautionary measures from prediction rates. The classification of algorithms used in this study is to assess trends and patterns that are assessed by BayesNet, NaiveBayes, J48, JRip, OneR and Decision Table. The output that has been used in this study, are correct classification, incorrect classification, True Positive Rate (TP), False Positive Rate (FP), Precision (P), Recall (R) and Fmeasure (F). These outputs are captured by using two different test methods: k-fold cross-validation and percentage split. Outputs are then compared to understand the classifier performances. Our analysis illustrates that JRip has classified the highest number of correct classifications by 73.71% followed by decision table with 73.66% of correct predictions, whereas OneR produced the least number of correct predictions with 64.95%. NaiveBayes took the least time of 0.57 sec to build the model and perform classification when compared to all the classifiers. The classifier stands out producing better results among all the classification methods. This study would be helpful for security agencies and police department to discover data patterns and analyze trending criminal activity from prediction rates.

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Incorrect attribute value detection for traffic accident data

Cited by 5 publications

References 9 publications

Customer Classification in Indian Retail Sector- A Comparative Analysis of Various Machine Learning Approaches

Customer Classification in Indian Retail Sector- A Comparative Analysis of Various Machine Learning Approaches

Noisy values detection and correction of traffic accident data

A Comparative Study of Classification Algorithms using Data Mining: Crime and Accidents in Denver City the USA

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