12 accepted papers
We propose a method that sequentially acquires features and selects a classifier for label assignment in data instances of related variables. The objective is to accurately infer the values of such variables (labels), ensuring at the same time that the expected total feature acquisition cost is mini…
We present a sequential acquisition of features and experts framework for datum–wise classification. The goal is to accurately assign labels for each instance, minimizing the acquisition cost of features and experts. An expert uses domain knowledge to make decisions. Starting from a prior belief, fe…
Administrative data collected by homeless service providers offer a unique opportunity to understand how homeless individuals navigate the homeless system towards securing stable housing. However, the literature on predictive models in the context of homeless service provision has neglected the sequ…
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 for sequential datum–wise joint feature selection and classification. Our proposed approach sequentially acquires features one at a time during testing until it decides that acquiring more features will not improve label assignment. At that point,…
We present metamer identification plus (metaID+), an algorithm that enhances the performance of brain-computer interface (BCI)-based color vision assessment. BCI-based color vision assessment uses steady-state visual evoked potentials (SSVEPs) elicited during a grid search of colors to identify meta…
We present CCA <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CUSUM</inf> , a classifier for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) that determines whether a user is attending to a flickering stimulus or is…
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…
Cyberbullying has emerged as a serious societal and public health problem that demands accurate methods for the detection of cyber-bullying instances in an effort to mitigate the consequences. While techniques to automatically detect cyberbullying incidents have been developed, the scalability and t…