• Korean Journal of Agricultural and Forest Meteorology
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• v.4 no.1
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• pp.23-28
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• 2002

Sets of weather variables for estimation of LWD were evaluated using CART(Classification And Regression Tree) models. Input variables were sets of hourly observations of air temperature at 0.3-m and 1.5-m height, relative humidity(RH), and wind speed that were obtained from May to September in 1997, 1998, and 1999 at 15 weather stations in iowa, Illinois, and Nebraska, USA. A model that included air temperature at 0.3-m height, RH, and wind speed showed the lowest misidentification rate for wetness. The model estimated presence or absence of wetness more accurately (85.5%) than the CART/SLD model (84.7%) proposed by Gleason et al. (1994). This slight improvement, however, was insufficient to justify the use of our model, which requires additional measurements, in preference to the CART/SLD model. This study demonstrated that the use of measurements of temperature, humidity, and wind from automated stations was sufficient to make LWD estimations of reasonable accuracy when the CART/SLD model was used. Therefore, implementation of crop disease-warning systems may be facilitated by application of the CART/SLD model that inputs readily obtainable weather observations.

• Journal of the Korea Society of Computer and Information
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• v.13 no.6
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• pp.51-58
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• 2008

A version control system is used in a rapidly changed environment or a program which developed in a complicated environment. it is a problem of configuration thread in supporting information that we, in this method, can't know a exactly well-defined configuration rule information and a predefined information. In this paper. Library system is suggested, modelled, and implemented so as to configuration thread control the components required by the user in many ways. As for the library used in the configuration thread control suggested in this paper, the components can be retrieved from the library regardless of the infrastructure, applying the extended facet classification. This retrieval framework is managed using TreeSearch class and the configuration thread control function. The library system of this paper can be used by the interface with other languages, and this system is to have a advantage to extend a facet by user.

• Tunnel and Underground Space
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• v.31 no.6
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• pp.508-519
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• 2021

In this study, a model was developed to predict the peak particle velocity (PPV) that affects people and the surrounding environment during blasting. Four machine learning models using the k-nearest neighbors (kNN), classification and regression tree (CART), support vector regression (SVR), and particle swarm optimization (PSO)-SVR algorithms were developed and compared with each other to predict the PPV. Mt. Yogmang located in Changwon-si, Gyeongsangnam-do was selected as a study area, and 1048 blasting data were acquired to train the machine learning models. The blasting data consisted of hole length, burden, spacing, maximum charge per delay, powder factor, number of holes, ratio of emulsion, monitoring distance and PPV. To evaluate the performance of the trained models, the mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were used. The PSO-SVR model showed superior performance with MAE, MSE and RMSE of 0.0348, 0.0021 and 0.0458, respectively. Finally, a method was proposed to predict the degree of influence on the surrounding environment using the developed machine learning models.

• The KIPS Transactions:PartB
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• v.13B no.7 s.110
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• pp.643-652
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• 2006

Decision trees are mainly used for the classification and prediction in data mining. The distribution of spatial data and relationships with their neighborhoods are very important when conducting classification for spatial data mining in the real world. Spatial decision trees in previous works have been designed for reflecting spatial data characteristic by rating Euclidean distance. But it only explains the distance of objects in spatial dimension so that it is hard to represent the distribution of spatial data and their relationships. This paper proposes a decision tree based on spatial entropy that represents the distribution of spatial data with the dispersion and dissimilarity. The dispersion presents the distribution of spatial objects within the belonged class. And dissimilarity indicates the distribution and its relationship with other classes. The rate of dispersion by dissimilarity presents that how related spatial distribution and classified data with non-spatial attributes we. Our experiment evaluates accuracy and building time of a decision tree as compared to previous methods. We achieve an improvement in performance by about 18%, 11%, respectively.

• Journal of KIISE:Software and Applications
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• v.26 no.11
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• pp.1314-1323
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• 1999

컴퓨터의 사용이 일반화됨에 따라 데이타를 생성하고 수집하는 것이 용이해졌다. 이에 따라 데이타로부터 자동적으로 유용한 지식을 얻는 기술이 필요하게 되었다. 데이타 마이닝에서 얻어진 지식은 정확성과 이해성을 충족해야 한다. 본 논문에서는 데이타 마이닝을 위하여 퍼지 결정트리에 기반한 효율적인 퍼지 규칙을 생성하는 알고리즘을 제안한다. 퍼지 결정트리는 ID3와 C4.5의 이해성과 퍼지이론의 추론과 표현력을 결합한 방법이다. 특히, 퍼지 규칙은 속성 축에 평행하게 판단 경계선을 결정하는 방법으로는 어려운 속성 축에 평행하지 않는 경계선을 갖는 패턴을 효율적으로 분류한다. 제안된 알고리즘은 첫째, 각 속성 데이타의 히스토그램 분석을 통해 적절한 소속함수를 생성한다. 둘째, 주어진 소속함수를 바탕으로 ID3와 C4.5와 유사한 방법으로 퍼지 결정트리를 생성한다. 또한, 유전자 알고리즘을 이용하여 소속함수를 조율한다. IRIS 데이타, Wisconsin breast cancer 데이타, credit screening 데이타 등 벤치마크 데이타들에 대한 실험 결과 제안된 방법이 C4.5 방법을 포함한 다른 방법보다 성능과 규칙의 이해성에서 보다 효율적임을 보인다.Abstract With an extended use of computers, we can easily generate and collect data. There is a need to acquire useful knowledge from data automatically. In data mining the acquired knowledge needs to be both accurate and comprehensible. In this paper, we propose an efficient fuzzy rule generation algorithm based on fuzzy decision tree for data mining. We combine the comprehensibility of rules generated based on decision tree such as ID3 and C4.5 and the expressive power of fuzzy sets. Particularly, fuzzy rules allow us to effectively classify patterns of non-axis-parallel decision boundaries, which are difficult to do using attribute-based classification methods.In our algorithm we first determine an appropriate set of membership functions for each attribute of data using histogram analysis. Given a set of membership functions then we construct a fuzzy decision tree in a similar way to that of ID3 and C4.5. We also apply genetic algorithm to tune the initial set of membership functions. We have experimented our algorithm with several benchmark data sets including the IRIS data, the Wisconsin breast cancer data, and the credit screening data. The experiment results show that our method is more efficient in performance and comprehensibility of rules compared with other methods including C4.5.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.397-411
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• 2023

The number of deteriorated bridges with a service period of more than 30 years has been rapidly increasing in Korea. Accordingly, the importance of advanced maintenance technologies through the predictions of age-induced deterioration degree, condition, and performance of bridges is more and more noticed. The prediction method of the safety grade of bridges was proposed in this study using the classification models of the Decision Tree and the Random Forest based on machine learning. As a result of analyzing these models for the 8,850 bridges located in national roads with various evaluation indexes such as confusion matrix, balanced accuracy, recall, ROC curve, and AUC, the Random Forest largely showed better predictive performance than that of the Decision Tree. In particular, random under-sampling in the Random Forest showed higher predictive performance than that of other sampling techniques for the C and D grade bridges, with the recall of 83.4%, which need more attention to maintenance because of the significant deterioration degree. The proposed model can be usefully applied to rapidly identify the safety grade and to establish an efficient and economical maintenance plan of bridges that have not recently been inspected.

• The Journal of Korea Robotics Society
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• v.15 no.2
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• pp.169-176
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• 2020

This paper studied the collision detection of robot manipulators for safe collaboration in human-robot interaction. Based on sensor-based collision detection, external torque is detached from subtracting robot dynamics. To detect collision using joint torque sensor data, a comparative study was conducted using data-based machine learning algorithm. Data was collected from the actual 3 degree-of-freedom (DOF) robot manipulator, and the data was labeled by threshold and handwork. Using support vector machine (SVM), decision tree and k-nearest neighbors KNN method, we derive the optimal parameters of each algorithm and compare the collision classification performance. The simulation results are analyzed for each method, and we confirmed that by an optimal collision status detection model with high prediction accuracy.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.578-580
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• 1998

The paper presents an approach to the structure identification based on the CART (Classification And Regression Tree) algorithm and to the parameter identification by hybrid learning method in neuro-fuzzy system. By using the CART algorithm, the proposed method can roughly estimate the numbers of membership function and fuzzy rule using the centers of decision regions. Then the parameter identification is carried out by the hybrid learning scheme using BP (Back-propagation) and RLSE (Recursive Least Square Estimation) from the numerical data. Finally, we will show it's usefulness for fuzzy modeling to truck backer upper control.

• Communications for Statistical Applications and Methods
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• v.13 no.2
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• pp.449-466
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• 2006

We propose to use variable selection methods based on penalized regression for pruning decision tree ensembles. Pruning methods based on LASSO and SCAD are compared with the cluster pruning method. Comparative studies are performed on some artificial datasets and real datasets. According to the results of comparative studies, the proposed methods based on penalized regression reduce the size of boosting ensembles without decreasing accuracy significantly and have better performance than the cluster pruning method. In terms of classification noise, the proposed pruning methods can mitigate the weakness of AdaBoost to some degree.

• Bulletin of the Korean Chemical Society
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• v.6 no.4
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• pp.241-246
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• 1985

Twenty trace elements were determined in 250 Korean potsherds and 5 clay samples by instrumental NAA. In the absence of identified samples of known origin, the potsherds were classified by a hierarchical centroid sorting method to construct a dendrogram. From this dendrogram 61 well-defined samples were selected to form 8 subclasses and five elements such as Cr, Cs, Sm, Sc and Th were supposed to be the main contributors for the classification. The 61 samples along with 5 clay samples were reclassified by means of minimal spanning tree as well as the hierarchical centroid sorting method by using 5 elements selected. As the results, the potsherds of certain classes defined in this work could be taken as a basis for latter identification and served as batches of identified species.