A comparative study of decision tree, random forest, and convolutional neural network for spread-F identification

In order to realize the non-destructive intelligent identification of weld surface defects, an intelligent recognition method based on deep learning is proposed, which is mainly formed by convolutional neural network (CNN) and forest random. First, the high-level features are automatically learned through the CNN. Random forest is trained with extracted high-level features to predict the classification results. Secondly, the weld surface defects images are collected and preprocessed by image enhancement and threshold segmentation. A database of weld surface defects is established using pre-processed images. Finally, comparative experiments are performed on the weld surface defects database. The results show that the accuracy of the method combined with CNN and random forest can reach 0.9875, and it also demonstrates the method is effective and practical.

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Convolutional neural network–bagged decision tree: a hybrid approach to reduce electric vehicle’s driver’s range anxiety by estimating energy consumption in real-time

Soft Computing ◽

10.1007/s00500-020-05310-y ◽

2020 ◽

Author(s):

Shatrughan Modi ◽

Jhilik Bhattacharya ◽

Prasenjit Basak

Keyword(s):

Neural Network ◽

Energy Consumption ◽

Decision Tree ◽

Convolutional Neural Network ◽

Real Time ◽

Hybrid Approach ◽

Range Anxiety

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Real-time usage forecasting for bike-sharing systems: A study on random forest and convolutional neural network applicability

2017 Intelligent Systems Conference (IntelliSys) ◽

10.1109/intellisys.2017.8324359 ◽

2017 ◽

Cited By ~ 3

Author(s):

Simon Ruffieux ◽

Nicolas Spycher ◽

Elena Mugellini ◽

Omar Abou Khaled

Keyword(s):

Neural Network ◽

Random Forest ◽

Convolutional Neural Network ◽

Real Time ◽

Time Usage ◽

Bike Sharing

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Predict plant-derived xenomiRs from plant miRNA sequences using random forest and one-dimensional convolutional neural network models

10.1101/345249 ◽

2018 ◽

Cited By ~ 1

Author(s):

Qi Zhao ◽

Qian Mao ◽

Zheng Zhao ◽

Tongyi Dou ◽

Zhiguo Wang ◽

...

Keyword(s):

Neural Network ◽

Random Forest ◽

Convolutional Neural Network ◽

Human Body ◽

Expression Profiles ◽

Mirna Sequence ◽

Selective Absorption ◽

Sequence Specificity ◽

One Dimensional ◽

Plant Mirna

AbstractBackgroundAn increasing number of studies reported that exogenous miRNAs (xenomiRs) can be detected in animal bodies, however, some others reported negative results. Some attributed this divergence to the selective absorption of plant-derived xenomiRs by animals.ResultsHere, we analyzed 166 plant-derived xenomiRs reported in our previous study and 942 non-xenomiRs extracted from miRNA expression profiles of four species of commonly consumed plants. Employing statistics analysis and cluster analysis, our study revealed the potential sequence specificity of plant-derived xenomiRs. Furthermore, a random forest model and a one-dimensional convolutional neural network model were trained using miRNA sequence features and raw miRNA sequences respectively and then employed to predict unlabeled plant miRNAs in miRBase. A total of 241 possible plant-derived xenomiRs were predicted by both models. Finally, the potential functions of these possible plant-derived xenomiRs along with our previously reported ones in human body were analyzed.ConclusionsOur study, for the first time, presents the systematic plant-derived xenomiR sequences analysis and provides evidence for selective absorption of plant miRNA by human body, which could facilitate the future investigation about the mechanisms underlying the transference of plant-derived xenomiR.

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Generalized Sparse Convolutional Neural Networks for Semantic Segmentation of Point Clouds Derived from Tri-Stereo Satellite Imagery

Remote Sensing ◽

10.3390/rs12081289 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1289

Author(s):

Stefan Bachhofner ◽

Ana-Maria Loghin ◽

Johannes Otepka ◽

Norbert Pfeifer ◽

Michael Hornacek ◽

...

Keyword(s):

Neural Network ◽

Decision Tree ◽

Convolutional Neural Network ◽

Semantic Segmentation ◽

Point Clouds ◽

Representation Learning ◽

Geometric Features ◽

Color Information ◽

Geometric Information ◽

Learning Technique

We studied the applicability of point clouds derived from tri-stereo satellite imagery for semantic segmentation for generalized sparse convolutional neural networks by the example of an Austrian study area. We examined, in particular, if the distorted geometric information, in addition to color, influences the performance of segmenting clutter, roads, buildings, trees, and vehicles. In this regard, we trained a fully convolutional neural network that uses generalized sparse convolution one time solely on 3D geometric information (i.e., 3D point cloud derived by dense image matching), and twice on 3D geometric as well as color information. In the first experiment, we did not use class weights, whereas in the second we did. We compared the results with a fully convolutional neural network that was trained on a 2D orthophoto, and a decision tree that was once trained on hand-crafted 3D geometric features, and once trained on hand-crafted 3D geometric as well as color features. The decision tree using hand-crafted features has been successfully applied to aerial laser scanning data in the literature. Hence, we compared our main interest of study, a representation learning technique, with another representation learning technique, and a non-representation learning technique. Our study area is located in Waldviertel, a region in Lower Austria. The territory is a hilly region covered mainly by forests, agriculture, and grasslands. Our classes of interest are heavily unbalanced. However, we did not use any data augmentation techniques to counter overfitting. For our study area, we reported that geometric and color information only improves the performance of the Generalized Sparse Convolutional Neural Network (GSCNN) on the dominant class, which leads to a higher overall performance in our case. We also found that training the network with median class weighting partially reverts the effects of adding color. The network also started to learn the classes with lower occurrences. The fully convolutional neural network that was trained on the 2D orthophoto generally outperforms the other two with a kappa score of over 90% and an average per class accuracy of 61%. However, the decision tree trained on colors and hand-crafted geometric features has a 2% higher accuracy for roads.

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Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers

Remote Sensing ◽

10.3390/rs11232788 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2788 ◽

Cited By ~ 4

Author(s):

Uwe Knauer ◽

Cornelius Styp von Rekowski ◽

Marianne Stecklina ◽

Tilman Krokotsch ◽

Tuan Pham Minh ◽

...

Keyword(s):

Neural Network ◽

Random Forest ◽

Convolutional Neural Network ◽

Tree Species ◽

Classification Performance ◽

Majority Voting ◽

Imaging Data ◽

Species Classification ◽

Weighted Voting ◽

Linear Discriminant

In this paper, we evaluate different popular voting strategies for fusion of classifier results. A convolutional neural network (CNN) and different variants of random forest (RF) classifiers were trained to discriminate between 15 tree species based on airborne hyperspectral imaging data. The spectral data was preprocessed with a multi-class linear discriminant analysis (MCLDA) as a means to reduce dimensionality and to obtain spatial–spectral features. The best individual classifier was a CNN with a classification accuracy of 0.73 +/− 0.086. The classification performance increased to an accuracy of 0.78 +/− 0.053 by using precision weighted voting for a hybrid ensemble of the CNN and two RF classifiers. This voting strategy clearly outperformed majority voting (0.74), accuracy weighted voting (0.75), and presidential voting (0.75).

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Deep Convolutional Neural Network for Mapping Smallholder Agriculture Using High Spatial Resolution Satellite Image

Sensors ◽

10.3390/s19102398 ◽

2019 ◽

Vol 19 (10) ◽

pp. 2398 ◽

Cited By ~ 7

Author(s):

Bin Xie ◽

Hankui K. Zhang ◽

Jie Xue

Keyword(s):

Neural Network ◽

Random Forest ◽

Land Cover ◽

Convolutional Neural Network ◽

Satellite Images ◽

Spatial Information ◽

Deep Convolutional Neural Network ◽

Crop Types ◽

Pooling Strategy

In classification of satellite images acquired over smallholder agricultural landscape with complex spectral profiles of various crop types, exploring image spatial information is important. The deep convolutional neural network (CNN), originally designed for natural image recognition in the computer vision field, can automatically explore high level spatial information and thus is promising for such tasks. This study tried to evaluate different CNN structures for classification of four smallholder agricultural landscapes in Heilongjiang, China using pan-sharpened 2 m GaoFen-1 (meaning high resolution in Chinese) satellite images. CNN with three pooling strategies: without pooling, with max pooling and with average pooling, were evaluated and compared with random forest. Two different numbers (~70,000 and ~290,000) of CNN learnable parameters were examined for each pooling strategy. The training and testing samples were systematically sampled from reference land cover maps to ensure sample distribution proportional to the reference land cover occurrence and included 60,000–400,000 pixels to ensure effective training. Testing sample classification results in the four study areas showed that the best pooling strategy was the average pooling CNN and that the CNN significantly outperformed random forest (2.4–3.3% higher overall accuracy and 0.05–0.24 higher kappa coefficient). Visual examination of CNN classification maps showed that CNN can discriminate better the spectrally similar crop types by effectively exploring spatial information. CNN was still significantly outperformed random forest using training samples that were evenly distributed among classes. Furthermore, future research to improve CNN performance was discussed.

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