A machine-learning framework for predicting multiple air pollutants' concentrations via multi-target regression and feature selection

This publication has 17 references indexed in Scilit:

• Multi-target regression via input space expansion: treating targets as inputs
  Machine Learning, 2016
• Computational personality recognition in social media
  User Modeling and User-Adapted Interaction, 2016
• Development of artificial intelligence based NO 2 forecasting models at Taj Mahal, Agra
  Atmospheric Pollution Research, 2015
• Tree ensembles for predicting structured outputs
  Pattern Recognition, 2013
• Dynamic ensemble extreme learning machine based on sample entropy
  Soft Computing, 2012
• Empirical study of feature selection methods based on individual feature evaluation for classification problems
  Expert Systems with Applications, 2011
• Variable selection using random forests
  Pattern Recognition Letters, 2010
• Forecasting hourly PM10 concentration in Cyprus through artificial neural networks and multiple regression models: implications to local environmental management
  Environmental Science and Pollution Research, 2010
• Using single- and multi-target regression trees and ensembles to model a compound index of vegetation condition
  Ecological Modelling, 2009
• On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario
  Sensors and Actuators B: Chemical, 2008