An Integrated Handheld Electronic Nose for Identifying Liquid Volatile Chemicals Using Improved Gradient-Boosting Decision Tree Methods

Classification of Data from Electronic Nose Using Gradient Tree Boosting Algorithm

In this paper, an approach that can fast classify the data from the electronic nose is presented. In this approach the gradient tree boosting algorithm is used to classify the gas data and the experiment results show that the proposed gradient tree boosting algorithm achieved high performance on this classification problem, outperforming other algorithms as comparison. In addition, electronic n...

Fruit Volatile Analysis Using an Electronic Nose

Numerous and diverse physiological changes occur during fruit ripening, including the development of a specific volatile blend that characterizes fruit aroma. Maturity at harvest is one of the key factors influencing the flavor quality of fruits and vegetables. The validation of robust methods that rapidly assess fruit maturity and aroma quality would allow improved management of advanced breed...

Stabilizing volatile liquid chemicals using co-crystallization.

A convenient, effective, and scalable protocol for stabilizing volatile liquid chemicals is reported. Through the use of halogen-bond driven co-crystal synthesis, several examples of liquid iodoperfluoroalkanes are transformed into crystalline materials with low-vapor pressure, considerable thermal stability, and moisture resistance. The target compounds can subsequently be re-generated through...

Phone duration modeling using gradient tree boosting

LightGBM: A Highly Efficient Gradient Boosting Decision Tree

Gradient Boosting Decision Tree (GBDT) is a popular machine learning algorithm, and has quite a few effective implementations such as XGBoost and pGBRT. Although many engineering optimizations have been adopted in these implementations, the efficiency and scalability are still unsatisfactory when the feature dimension is high and data size is large. A major reason is that for each feature, they...