News Classification for Identifying Traffic Incident Points in a Spanish-Speaking Country: A Real-World Case Study of Class Imbalance Learning

Rivera, Gilberto; Florencia, Rogelio; García, Vicente; Ruiz, Alejandro; Sánchez-Solís, J. Patricia

doi:10.3390/app10186253

Cited by 16 publications

(13 citation statements)

References 62 publications

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“…), synthetic minority oversampling technique (SMOTE), random undersampling, random oversampling and borderline SMOTE. Consequently, the final classifier reached ‹ 10 › a sensitivity of 0.86 and an area under the precision-recall curve of 0.86, which is generally acceptable considering the complexity level of assessing unstructured texts in Spanish (Rivera et al, 2020).…”

Section: Data Balancing Techniques In Crash Severity Prediction Modelingmentioning

confidence: 85%

“…However, another disadvantage of oversampling is that it increases the number of training observations, thus increasing the learning time (Cover & Hart, 1967). Gilberto Rivera et al (2020) studied the application of machine-learning algorithms and natural-language processing to the news provided by the RSS service. Their goal was to classify them based on whether they were about a traffic incident or otherwise in order to notify citizens where such accidents had specifically occurred.…”

Section: Data Balancing Techniques In Crash Severity Prediction Modelingmentioning

confidence: 99%

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An assessment of machine learning and data balancing techniques for evaluating downgrade truck crash severity prediction in Wyoming

Ampadu

Haq

Ksaibati

2022

J. sustain. dev. transp. logist.

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This study involved the investigation of various machine learning methods, including four classification tree-based ML models, namely the Adaptive Boosting tree, Random Forest, Gradient Boost Decision Tree, Extreme Gradient Boosting tree, and three non-tree-based ML models, namely Support Vector Machines, Multi-layer Perceptron and k-Nearest Neighbors for predicting the level of severity of large truck crashes on Wyoming road networks. The accuracy of these seven methods was then compared. The Final ROC AUC score for the optimized random forest model is 95.296 %. The next highest performing model was the k-NN with 92.780 %, M.L.P. with 87.817 %, XGBoost with 86.542 %, Gradboost with 74.824 %, SVM with 72.648 % and AdaBoost with 67.232 %. Based on the analysis, the top 10 predictors of severity were obtained from the feature importance plot. These may be classified into whether safety equipment was used, whether airbags were deployed, the gender of the driver and whether alcohol was involved.

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Section: Data Balancing Techniques In Crash Severity Prediction Modelingmentioning

confidence: 85%

Section: Data Balancing Techniques In Crash Severity Prediction Modelingmentioning

confidence: 99%

An assessment of machine learning and data balancing techniques for evaluating downgrade truck crash severity prediction in Wyoming

Ampadu

Haq

Ksaibati

2022

J. sustain. dev. transp. logist.

View full text Add to dashboard Cite

show abstract

“…In this regard, the literature is quite scarce. Rivera et al propose a classiier for RSS-like feed of news that is able to distinguish and locate traic incidents, in order to timely alert citizens [53]. Similarly, Abid et al propose to train a classiier to recognize events where some form of life loss happened (e.g.…”

Section: Smart Cities: Overview and Frameworkmentioning

confidence: 99%

A News-Based Framework for Uncovering and Tracking City Area Profiles: Assessment in Covid-19 Setting

Bechini

Bondielli

Bárcena

et al. 2022

ACM Trans. Knowl. Discov. Data

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In the last years, there has been an ever-increasing interest in profiling various aspects of city life, especially in the context of smart cities. This interest has become even more relevant recently when we have realised how dramatic events, such as the Covid-19 pandemic, can deeply affect the city life, producing drastic changes. Identifying and analyzing such changes, both at the city level and within single neighborhoods, may be a fundamental tool to better manage the current situation and provide sound strategies for future planning. Furthermore, such fine-grained and up-to-date characterization can represent a valuable asset for other tools and services, e.g. web mapping applications or real estate agency platforms. In this paper, we propose a framework featuring a novel methodology to model and track changes in areas of the city by extracting information from online newspaper articles. The problem of uncovering clusters of news at specific times is tackled by means of the joint use of state-of-the-art language models to represent the articles, and of a density-based streaming clustering algorithm, properly shaped to deal with high-dimensional text embeddings. Further, we propose a method to automatically label the obtained clusters in a semantically meaningful way, and we introduce a set of metrics aimed to track the temporal evolution of clusters. A case study focusing on the city of Rome during the Covid-19 pandemic is illustrated and discussed to evaluate the effectiveness of the proposed approach.

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“…The authors solve such problems by first converting the test cases to numeric vectors using NLP techniques and then applying supervised learning for imbalanced datasets on the resulting vectors. Rivera et al (2020) classify news articles in a local Spanish newspaper as traffic-related or not. The authors first convert the article into a vectorized data using bag-of-words and TF-IDF (term frequency-inverse document frequency) techniques.…”

Section: Related Workmentioning

confidence: 99%

“…The class imbalance is dealt with via different sampling methods. The final classifier achieves a sensitivity of 0.86 (Rivera et. al.…”

Section: Related Workmentioning

confidence: 99%

Extracting Features from Textual Data in Class Imbalance Problems

Aravamuthan

Jogalekar

Lee

2022

J. Comp. Assist. Linguist. Res.

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We address class imbalance problems. These are classification problems where the target variable is binary, and one class dominates over the other. A central objective in these problems is to identify features that yield models with high precision/recall values, the standard yardsticks for assessing such models. Our features are extracted from the textual data inherent in such problems. We use n-gram frequencies as features and introduce a discrepancy score that measures the efficacy of an n-gram in highlighting the minority class. The frequency counts of n-grams with the highest discrepancy scores are used as features to construct models with the desired metrics. According to the best practices followed by the services industry, many customer support tickets will get audited and tagged as “contract-compliant” whereas some will be tagged as “over-delivered”. Based on in-field data, we use a random forest classifier and perform a randomized grid search over the model hyperparameters. The model scoring is performed using an scoring function. Our objective is to minimize the follow-up costs by optimizing the recall score while maintaining a base-level precision score. The final optimized model achieves an acceptable recall score while staying above the target precision. We validate our feature selection method by comparing our model with one constructed using frequency counts of n-grams chosen randomly. We propose extensions of our feature extraction method to general classification (binary and multi-class) and regression problems. The discrepancy score is one measure of dissimilarity of distributions and other (more general) measures that we formulate could potentially yield more effective models.

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News Classification for Identifying Traffic Incident Points in a Spanish-Speaking Country: A Real-World Case Study of Class Imbalance Learning

Cited by 16 publications

References 62 publications

An assessment of machine learning and data balancing techniques for evaluating downgrade truck crash severity prediction in Wyoming

An assessment of machine learning and data balancing techniques for evaluating downgrade truck crash severity prediction in Wyoming

A News-Based Framework for Uncovering and Tracking City Area Profiles: Assessment in Covid-19 Setting

Extracting Features from Textual Data in Class Imbalance Problems

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