Decision Tree

Naylor, Mike

doi:10.5951/mtlt.2020.0081

Cited by 2 publications

(6 citation statements)

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“…Table 1 shows the comparison of the precision, recall, and F1-score of the proposed approach using the most standard classification approaches (KNN [21], NB [22], SVM [23], DT [24], QDA [25], RF [26]). The QDA [25] based on Delaunay triangulation of landmark attains a promising precision as compared to other classifiers with 100% on CK+ database.…”

Section: Evaluation and Comparison Regarding Accuracymentioning

confidence: 99%

“…Tables 8 shows a comparison of the similarity measures on CK+ Dataset for six classifiers. From that Table 8, it is noted that the value of similarity of QDA based on Delaunay triangulation is better than other classifiers (KNN [21], NB [22], SVM [23], DT [24], RF [26]). Such high similarity measure and low error indicated that the proposed Delaunay triangulation will achieve higher similarity values (100%) and is therefore suitable for facial expression recognition.…”

Section: Evaluation and Comparison Regarding Accuracymentioning

confidence: 99%

“…As it is shown in Table 13-15 in comparison of QDA, SVM, and DT with other classifiers, values of the confusion matrix of these classifiers are high, while the other classifiers KNN, NB, and RF for FER, are low. [21], NB [22], SVM [23], DT [24], and RF [26]).…”

Section: Evaluation and Comparison Regarding Accuracymentioning

confidence: 99%

“…Figure 6. Samples of CK + database 5.4.1 Evaluation and comparison regarding accuracyTable1shows the comparison of the precision, recall, and F1-score of the proposed approach using the most standard classification approaches (KNN[21], NB[22], SVM[23], DT[24], QDA[25], RF[26]). The QDA[25] based on Delaunay triangulation of landmark attains a promising precision as…”

mentioning

confidence: 99%

“…Simples of JAFFE database 5.6.1 Evaluation and comparison regarding accuracyIn Table17, accuracy, recall, and F1-score values are computed for all six classifiers based on Delaunay triangulation during the classification phase. The obtained results of SVM are slightly better than other classifiers (KNN[21], NB[22], DT[24], RF[26]) as their higher values are respectively 71.43, 73.57 and 33.61. SVM succeed on JAFEE database.…”

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confidence: 99%

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Facial Expressions Recognition Based on Delaunay Triangulation of Landmark and Machine Learning

Ayeche¹,

Alti²

2021

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Facial expressions can tell a lot about an individual’s emotional state. Recent technological advances opening avenues for automatic Facial Expression Recognition (FER) based on machine learning techniques. Many works have been done on FER for the classification of facial expressions. However, the applicability to more naturalistic facial expressions remains unclear. This paper intends to develop a machine learning approach based on the Delaunay triangulation to extract the relevant facial features allowing classifying facial expressions. Initially, from the given facial image, a set of discriminative landmarks are extracted. Along with this, a minimal landmark connected graph is also extracted. Thereby, from the connected graph, the expression is represented by a one-dimensional feature vector. Finally, the obtained vector is subject for classification by six well-known classifiers (KNN, NB, DT, QDA, RF and SVM). The experiments are conducted on four standard databases (CK+, KDEF, JAFFE and MUG) to evaluate the performance of the proposed approach and find out which classifier is better suited to our system. The QDA approach based on the Delaunay triangulation has a high accuracy of 96.94% since it only supports non-zero pixels, which increases the recognition rate.

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Section: Evaluation and Comparison Regarding Accuracymentioning

confidence: 99%

Section: Evaluation and Comparison Regarding Accuracymentioning

confidence: 99%

Section: Evaluation and Comparison Regarding Accuracymentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Facial Expressions Recognition Based on Delaunay Triangulation of Landmark and Machine Learning

Ayeche¹,

Alti²

2021

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Study on thermal conductivity of improved soil under different freezing temperatures

Wang,

Li,

Xia

et al. 2023

PLoS ONE

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Based on the influence of moisture content, dry density and temperature (≦ 0°C) on the thermal conductivity of lime-modified red clay, the thermal conductivity was measured by transient hot wire method. A total of 125 data were obtained and the evolution law of thermal conductivity with influencing factors was analyzed. The fitting formula of thermal conductivity of lime-modified red clay and a variety of intelligent prediction models were established and compared with previous empirical formulas. The results show that the thermal conductivity of lime-modified red clay increases linearly with water content and dry density. The change of thermal conductivity with temperature is divided into three stages. In the first stage, the thermal conductivity increases slowly with the decrease of temperature in the temperature range of-2°Cto 0°C. In the second stage, in the temperature range of-5°Cto (-2)°C, the thermal conductivity increases rapidly with the decrease of temperature. In the third stage, in the range of-10°Cto (-5)°C, the thermal conductivity changes little with the decrease of temperature, and the fitting curve tends to be stable. The fitting formula model and various intelligent prediction models can realize the accurate prediction of the thermal conductivity of lime-improved soil. Using RMSE (Root Mean Square Error) and MAPE (Mean Absolute Percentage Error) to evaluate the model, it is found that the GBDT decision tree model has the best prediction effect, the RMSE value of the predicted value is 0.084, and the MAPE value is 4.1%. The previous empirical models have poor prediction effect on the thermal conductivity of improved red clay. The intelligent prediction models such as GBDT decision tree with strong universality and high prediction accuracy are recommended to predict the thermal conductivity of soil.

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Decision Tree

Cited by 2 publications

References 0 publications

Facial Expressions Recognition Based on Delaunay Triangulation of Landmark and Machine Learning

Facial Expressions Recognition Based on Delaunay Triangulation of Landmark and Machine Learning

Study on thermal conductivity of improved soil under different freezing temperatures

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