scholarly journals Legal Judgment Prediction Based on Machine Learning: Predicting the Discretionary Damages of Mental Suffering in Fatal Car Accident Cases

2021 ◽  
Vol 11 (21) ◽  
pp. 10361
Author(s):  
Decheng Hsieh ◽  
Lieuhen Chen ◽  
Taiping Sun
Keyword(s):  
Random Forests ◽  
Predictive Models ◽  
Nearest Neighbor ◽  
The Other ◽  
K Nearest Neighbor ◽  
Feature Importance ◽  
Importance Ranking ◽  
Classification And Regression ◽  
Car Accident ◽  
Neighbor Classification

The discretionary damage of mental suffering in fatal car accident cases in Taiwan is subjective, uncertain, and unpredictable; thus, plaintiffs, defendants, and their lawyers find it difficult to judge whether spending much of their money and time on the lawsuit is worthwhile and which legal factors judges will consider important and dominant when they are assessing the mental suffering damages. To address these problems, we propose k-nearest neighbor, classification and regression trees, and random forests as learning algorithms for regression to build optimal predictive models. In addition, we reveal the importance ranking of legal factors by permutation feature importance. The experimental results show that the random forest model outperformed the other models and achieved good performance, and “the mental suffering damages that plaintiff claims” and “the age of the victim” play important roles in assessments of mental suffering damages in fatal car accident cases in Taiwan. Therefore, litigants and their lawyers can predict the discretionary damages of mental suffering in advance and wisely decide whether they should litigate or not, and then they can focus on the crucial legal factors and develop the best litigation strategy.

scholarly journals Exclusive lasso-based k-nearest-neighbor classification

2021 ◽  
Author(s):  
Lin Qiu ◽  
Yanpeng Qu ◽  
Changjing Shang ◽  
Longzhi Yang ◽  
Fei Chao ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Classification ◽  
Neighbor Classification

scholarly journals Accurate white matter lesion segmentation by k nearest neighbor classification with tissue type priors (kNN-TTPs)

2013 ◽  
Vol 3 ◽  
pp. 462-469 ◽  
Author(s):  
Martijn D. Steenwijk ◽  
Petra J.W. Pouwels ◽  
Marita Daams ◽  
Jan Willem van Dalen ◽  
Matthan W.A. Caan ◽  
...  
Keyword(s):  
White Matter ◽  
Nearest Neighbor ◽  
White Matter Lesion ◽  
Tissue Type ◽  
Lesion Segmentation ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Classification ◽  
Neighbor Classification

scholarly journals Identification of Student Academic Performance using the KNN Algorithm

2020 ◽  
Vol 2 (3) ◽  
pp. 115-122
Author(s):  
Aldi Nugroho ◽  
Osvaldo Richie Riady ◽  
Alexander Calvin ◽  
Derwin Suhartono
Keyword(s):  
Graduation Rates ◽  
Teaching And Learning ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Student Academic Performance ◽  
Education Needs ◽  
Attention To Students ◽  
The University ◽  
Neighbor Classification ◽  
Previous Learning

Students are an important asset in the world of education also an institution and therefore also need to pay attention to students' graduation rates on time. The ups and downs of the percentage of students' abilities in classroom learning is one important element for assessing university accreditation. Therefore, it is necessary to monitor and evaluate teaching and learning activities using the KNN Algorithm classification. By processing student complaints data and seeing the results of previous learning can obtain important things for higher education needs. In predicting graduation rates based on complaints, this study uses the K-Nearest Neighbor classification algorithm by grouping data k = 1, k = 2, k = 3 with the smallest value possible. In experiments using the KNN method the results were clearly visible and showed quite good accuracy. From the experiment it was concluded that if there were fewer complaints from one student it could minimize the level of student non-graduates at the university and ultimately produce good accreditation.

scholarly journals Mangrove Forest Classification in Drone Images Using HSV Color Moment and Haralick Features Extraction with K-Nearest Neighbor

2019 ◽  
Vol 1 (3) ◽  
pp. 1-12
Author(s):  
Agus Wahyu Widodo ◽  
Deo Hernando ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Mangrove Forest ◽  
Nearest Neighbor ◽  
Classification Method ◽  
Mangrove Forests ◽  
K Nearest Neighbor ◽  
Distance Method ◽  
K Value ◽  
Nearest Neighbor Classification ◽  
Haralick Features ◽  
Neighbor Classification

Due to the problems with uncontrolled changes in mangrove forests, a forest function management and supervision is required. The form of mangrove forest management carried out in this study is to measure the area of mangrove forests by observing the forests using drones or crewless aircraft. Drones are used to take photos because they can capture vast mangrove forests with high resolution. The drone was flown over above the mangrove forest and took several photos. The method used in this study is extracting color features using mean values, standard deviations, and skewness in the HSV color space and texture feature extraction with Haralick features. The classification method used is the k-nearest neighbor method. This study conducted three tests, namely testing the accuracy of the system, testing the distance method used in the k-nearest neighbor classification method, and testing the k value. Based on the results of the three tests above, three conclusions obtained. The first conclusion is that the classification system produces an accuracy of 84%. The second conclusion is that the distance method used in the k-nearest neighbor classification method influences the accuracy of the system. The distance method that produces the highest accuracy is the Euclidean distance method with an accuracy of 84%. The third conclusion is that the k value used in the k-nearest neighbor classification method influences the accuracy of the system. The k-value that produces the highest accuracy is k = 3, with an accuracy of 84%.

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