61 citations · 167 across the 6 of their papers we have counts for
6 papers
Leveraging Elastic Demand for Forecasting
Houtao Deng, Ganesh Krishnan, Ji Chen +1
Demand variance can result in a mismatch between planned supply and actual demand. Demand shaping strategies such as pricing can be used to shift elastic demand to reduce the imbal…
Interpreting Tree Ensembles with inTrees
Houtao Deng
Tree ensembles such as random forests and boosted trees are accurate but difficult to understand, debug and deploy. In this work, we provide the inTrees (interpretable trees) frame…
Guided Random Forest in the RRF Package
Houtao Deng
Random Forest (RF) is a powerful supervised learner and has been popularly used in many applications such as bioinformatics. In this work we propose the guided random forest (GRF)…
A Time Series Forest for Classification and Feature Extraction
Houtao Deng, George Runger, Eugene Tuv +1
We propose a tree ensemble method, referred to as time series forest (TSF), for time series classification. TSF employs a combination of the entropy gain and a distance measure, re…
Gene selection with guided regularized random forest
Houtao Deng, George Runger
The regularized random forest (RRF) was recently proposed for feature selection by building only one ensemble. In RRF the features are evaluated on a part of the training data at e…
Feature Selection via Regularized Trees
Houtao Deng, George Runger
We propose a tree regularization framework, which enables many tree models to perform feature selection efficiently. The key idea of the regularization framework is to penalize sel…