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Social and Behavioral Sciences Commons™
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Full-Text Articles in Social and Behavioral Sciences
Predictive Taxonomy Analytics (Lasso): Predicting Outcome Types Of Cyber Breach, Jing Rong Goh, Shaun S. Wang, Yaniv Harel, Gabriel Toh
Predictive Taxonomy Analytics (Lasso): Predicting Outcome Types Of Cyber Breach, Jing Rong Goh, Shaun S. Wang, Yaniv Harel, Gabriel Toh
Research Collection School Of Economics
Cyber breaches are costly for the global economy and extensive efforts have gone into improving the cybersecurity infrastructure. There are numerous types of cyber breaches that vary greatly in terms of cause and impact, resulting in an extensive literature for individual cyber breach type. Our paper seeks to provide a general framework that can be easily applied to analyze different types of cyber breaches. Our framework is inspired by the taxonomy approach in the cybersecurity literature, where it was proposed that an effective set of taxonomy can provide a direction on supporting improved decision-making in cyber risk management and selecting …
Determining The Number Of Communities In Degree-Corrected Stochastic Block Models, Shujie Ma, Liangjun Su, Yichong Zhang
Determining The Number Of Communities In Degree-Corrected Stochastic Block Models, Shujie Ma, Liangjun Su, Yichong Zhang
Research Collection School Of Economics
We propose to estimate the number of communities in degree-corrected stochastic block models based on a pseudo likelihood ratio. For estimation, we consider a spectral clustering together with binary segmentation method. This approach guarantees an upper bound for the pseudo likelihood ratio statistic when the model is over-fitted. We also derive its limiting distribution when the model is under-fitted. Based on these properties, we establish the consistency of our estimator for the true number of communities. Developing these theoretical properties require a mild condition on the average degree: growing at a rate faster than log(n), where n is the number …
Evaluating Human Versus Machine Learning Performance In Classifying Research Abstracts, Yeow Chong Goh, Xin Qing Cai, Walter Theseira, Giovanni Ko, Khiam Aik Khor
Evaluating Human Versus Machine Learning Performance In Classifying Research Abstracts, Yeow Chong Goh, Xin Qing Cai, Walter Theseira, Giovanni Ko, Khiam Aik Khor
Research Collection School Of Economics
We study whether humans or machine learning (ML) classification models are better at classifying scientific research abstracts according to a fixed set of discipline groups. We recruit both undergraduate and postgraduate assistants for this task in separate stages, and compare their performance against the support vectors machine ML algorithm at classifying European Research Council Starting Grant project abstracts to their actual evaluation panels, which are organised by discipline groups. On average, ML is more accurate than human classifiers, across a variety of training and test datasets, and across evaluation panels. ML classifiers trained on different training sets are also more …