Combining Himawari-8 AOD and deep forest model to obtain city-level distribution of PM2.5 in China

2022 ◽  
pp. 118826
Author(s):  
Zhihao Song ◽  
Chen Bin ◽  
Jianping Huang
Keyword(s):  
Forest Model ◽  
Deep Forest

scholarly journals Multi-Feature Fusion for Weak Target Detection on Sea-Surface Based on FAR Controllable Deep Forest Model

2021 ◽  
Vol 13 (4) ◽  
pp. 812
Author(s):  
Jiahuan Zhang ◽  
Hongjun Song
Keyword(s):  
Target Detection ◽  
Feature Fusion ◽  
Feature Space ◽  
Sea Surface ◽  
Detection Accuracy ◽  
Forest Model ◽  
High Profile ◽  
Deep Forest ◽  
Positive Effect ◽  
Weak Target

Target detection on the sea-surface has always been a high-profile problem, and the detection of weak targets is one of the most difficult problems and the key issue under this problem. Traditional techniques, such as imaging, cannot effectively detect these types of targets, so researchers choose to start by mining the characteristics of the received echoes and other aspects for target detection. This paper proposes a false alarm rate (FAR) controllable deep forest model based on six-dimensional feature space for efficient and accurate detection of weak targets on the sea-surface. This is the first attempt at the deep forest model in this field. The validity of the model was verified on IPIX data, and the detection probability was compared with other proposed methods. Under the same FAR condition, the average detection accuracy rate of the proposed method could reach over 99.19%, which is 9.96% better than the results of the current most advanced method (K-NN FAR-controlled Detector). Experimental results show that multi-feature fusion and the use of a suitable detection framework have a positive effect on the detection of weak targets on the sea-surface.

scholarly journals An improved deep forest model for prediction of e-commerce consumers’ repurchase behavior

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0255906
Author(s):  
Weiwei Zhang ◽  
Mingyan Wang
Keyword(s):  
Prediction Models ◽  
Feature Model ◽  
The Internet ◽  
User Characteristics ◽  
Data Set ◽  
Training Time ◽  
Forest Model ◽  
Interactive Behavior ◽  
Deep Forest ◽  
Behavior Characteristics

As the Internet retail industry continues to rise, more and more consumers choose to shop online, especially Chinese consumers. Using consumer behavior data left on the Internet to predict repurchase behavior is of great significance for companies to achieve precision marketing. This paper proposes an improved deep forest model, and the interactive behavior characteristics of users and goods are added into the original feature model to predict the repurchase behavior of e-commerce consumers. Based on the Alibaba mobile e-commerce platform data set, first construct a feature engineering that includes user characteristics, product characteristics, and interactive behavior characteristics. And then use our proposed model to make predictions. Experiments show that the model’s overall performance with increased interactive behavior features is better and has higher accuracy. Compared with the existing prediction models, the improved deep forest model has certain advantages, which not only improves the prediction accuracy but also reduces the cost of training time.

scholarly journals SAR Target Classification Based on Deep Forest Model

2020 ◽  
Vol 12 (1) ◽  
pp. 128 ◽  
Author(s):  
Jiahuan Zhang ◽  
Hongjun Song ◽  
Binbin Zhou
Keyword(s):  
Information Acquisition ◽  
Target Identification ◽  
Image Features ◽  
Automatic Process ◽  
Model Parameters ◽  
Target Classification ◽  
Stationary Target ◽  
Practical Applications ◽  
Forest Model ◽  
Deep Forest

Synthetic aperture radar (SAR) has become one of the most important means of information acquisition in today’s society and shows great potential in many fields. Target identification and classification of SAR images are also the focus of research. With the vigorous development of deep learning, many researchers apply this method to SAR target classification to obtain a more automatic process and more accurate results. In this paper, a novel deep forest model constructed by multi-grained cascade forest (gcForest), which is different from the traditional neural network (NN) model, is employed to classify ten types of SAR targets in the moving and stationary target acquisition and recognition (MSTAR) dataset. Considering that the targets of input images may be off-center and of different sizes in practical applications, two improved models based on varying weights by image features have been put forward, and both obtain better results. A series of experiments have been conducted to optimize model parameters, and final results with the MSTAR dataset illustrate that the two improved models are both superior to the original gcForest model. This is the first attempt to classify SAR targets using the non-NN model.

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