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Full-Text Articles in Physical Sciences and Mathematics
Deep Air Learning: Interpolation, Prediction, And Feature Analysis Of Fine-Grained Air Quality, Zhongang Qi, Tianchun Wang, Guojie Song, Weisong Hu, Xi Li, Zhongfei Mark Zhang
Deep Air Learning: Interpolation, Prediction, And Feature Analysis Of Fine-Grained Air Quality, Zhongang Qi, Tianchun Wang, Guojie Song, Weisong Hu, Xi Li, Zhongfei Mark Zhang
Research Collection School Of Computing and Information Systems
The interpolation, prediction, and feature analysis of fine-gained air quality are three important topics in the area of urban air computing. The solutions to these topics can provide extremely useful information to support air pollution control, and consequently generate great societal and technical impacts. Most of the existing work solves the three problems separately by different models. In this paper, we propose a general and effective approach to solve the three problems in one model called the Deep Air Learning (DAL). The main idea of DAL lies in embedding feature selection and semi-supervised learning in different layers of the deep …
Sparse Modeling-Based Sequential Ensemble Learning For Effective Outlier Detection In High-Dimensional Numeric Data, Guansong Pang, Longbing Cao, Ling Chen, Defu Lian, Huan Liu
Sparse Modeling-Based Sequential Ensemble Learning For Effective Outlier Detection In High-Dimensional Numeric Data, Guansong Pang, Longbing Cao, Ling Chen, Defu Lian, Huan Liu
Research Collection School Of Computing and Information Systems
The large proportion of irrelevant or noisy features in reallife high-dimensional data presents a significant challenge to subspace/feature selection-based high-dimensional outlier detection (a.k.a. outlier scoring) methods. These methods often perform the two dependent tasks: relevant feature subset search and outlier scoring independently, consequently retaining features/subspaces irrelevant to the scoring method and downgrading the detection performance. This paper introduces a novel sequential ensemble-based framework SEMSE and its instance CINFO to address this issue. SEMSE learns the sequential ensembles to mutually refine feature selection and outlier scoring by iterative sparse modeling with outlier scores as the pseudo target feature. CINFO instantiates SEMSE …