5 accepted papers
Despite the popularity of complex machine learning models, domain experts often struggle to understand and are reluctant to trust them due to lack of intuition and explanation of their predictions. Moreover, these cannot be used in many real–world applications, where features are not readily availab…
We introduce a supervised machine learning framework to perform joint feature selection and classification individually for each data instance during testing. In contrast to our prior work, we decide both the order and the number of features for each data instance. Specifically, our proposed solutio…
Online feature selection and classification is crucial for time sensitive decision making. Existing work however either assumes that features are independent or produces a fixed number of features for classification. Instead, we propose an optimal framework to perform joint feature selection and cla…
Empowering citizens to interact directly with their local governments through civic engagement platforms has emerged as an easy way to resolve urban issues. However, for authorities to manually process reported issues is both impractical and inefficient; accurate, online and near-real-time processin…
In this paper, the problem of detecting freeway accidents in real-time based on speed readings from spatially distributed road sensors of variable accuracy is addressed. To ensure robust decision-making, a novel two-stage approach is proposed. Specifically, in the first stage, each sensor generates…