• Bulletin of the Korean Mathematical Society
• /
• v.45 no.1
• /
• pp.95-99
• /
• 2008

It is known that each eigenvalue of a real symmetric, irreducible, tridiagonal matrix has multiplicity 1. The graph of such a matrix is a path. In this paper, we extend the result by classifying those trees for which each of the associated acyclic matrices has distinct eigenvalues whenever the diagonal entries are distinct.

• 한국데이터정보과학회:학술대회논문집
• /
• 2004.10a
• /
• pp.63-75
• /
• 2004

The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud dection, data reduction and variable screening, category merging, etc. We analyze Gyeongnam social indicator survey data using decision tree techniques for environmental information. We can use these decision tree outputs for environmental preservation and improvement.

• Journal of the Korean Data and Information Science Society
• /
• v.15 no.4
• /
• pp.759-771
• /
• 2004

The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud dection, data reduction and variable screening, category merging, etc. We analyze Gyeongnam social indicator survey data using decision tree techniques for environmental information. We can use these decision tree outputs for environmental preservation and improvement.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2017.04a
• /
• pp.787-790
• /
• 2017

Data quality is the main issue in the classification problems; generally, the presence of noisy instances in the training dataset will not lead to robust classification performance. Such instances may cause the generated decision tree to suffer from over-fitting and its accuracy may decrease. Decision trees are useful, efficient, and commonly used for solving various real world classification problems in data mining. In this paper, we introduce a preprocessing technique to improve the classification accuracy rates of the C4.5 decision tree algorithm. In the proposed preprocessing method, we applied the naive Bayes classifier to remove the noisy instances from the training dataset. We applied our proposed method to a real e-commerce sales dataset to test the performance of the proposed algorithm against the existing C4.5 decision tree classifier. As the experimental results, the proposed method improved the classification accuracy by 8.5% and 14.32% using training dataset and 10-fold crossvalidation, respectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2008.08a
• /
• pp.155-159
• /
• 2008

In this thesis, in order to develop a new classification model of Sasang Constitutional medical types, which is helpful for improving the accuracy of diagnosis of medical types. various data-mining classification models such as discriminant analysis. decision trees analysis, neural networks analysis, logistics regression analysis, clustering analysis which are main classification methods were applied to the questionnaires of medical type classification. In this manner, a model which scientifically classifies constitutional medical types in the field of Sasang Constitutional Medicine, one of a traditional Korean medicine, has been developed. Also, the above-mentioned analysis models were systematically compared and analyzed. In this study, a classification of Sasang constitutional medical types was developed based on the discriminate analysis model and decision trees analysis model of which accuracy is relatively high, of which analysis procedure is easy to understand and to explain and which are easy to implement. Also, a diagnosis system of Sasang constitution was implemented applying the two analysis models.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.22 no.6
• /
• pp.1-14
• /
• 2019

Carbon storage is one of the regulating ecosystem services provided by urban street trees. It is important that evaluating the economic value of ecosystem services accurately. The carbon storage of street trees was calculated by measuring the morphological parameter on the field. As the method is labor-intensive and time-consuming for the macro-scale research, remote sensing has been more widely used. The airborne Light Detection And Ranging (LiDAR) is used in obtaining the point clouds data of a densely planted area and extracting individual trees for the carbon storage estimation. However, the LiDAR has limitations such as high cost and complicated operations. In addition, trees change over time they need to be frequently. Therefore, Structure from Motion (SfM) photogrammetry with unmanned Aerial Vehicle (UAV) is a more suitable method for obtaining point clouds data. In this paper, a UAV loaded with a digital camera was employed to take oblique aerial images for generating point cloud of street trees. We extracted the diameter of breast height (DBH) from generated point cloud data to calculate the carbon storage. We compared DBH calculated from UAV data and measured data from the field in the selected area. The calculated DBH was used to estimate the carbon storage of street trees in the study area using a regression model. The results demonstrate the feasibility and effectiveness of applying UAV imagery and SfM technique to the carbon storage estimation of street trees. The technique can contribute to efficiently building inventories of the carbon storage of street trees in urban areas.

• Journal of Korea Multimedia Society
• /
• v.18 no.11
• /
• pp.1261-1267
• /
• 2015

The problem of underwater target detection and classification has been attracted a substantial amount of attention and studied from many researchers for both military and non-military purposes. The difficulty is complicate due to various environmental conditions. In this paper, we study classifier ensemble methods for active sonar target classification to improve the classification performance. In general, classifier ensemble method is useful for classifiers whose variances relatively large such as decision trees and neural networks. Bagging, Random selection samples, Random subspace and Rotation forest are selected as classifier ensemble methods. Using the four ensemble methods based on 31 neural network classifiers, the classification tests were carried out and performances were compared.

• Journal of the Korean Data and Information Science Society
• /
• v.27 no.1
• /
• pp.255-264
• /
• 2016

Classification is a predictive modeling for a categorical target variable. Various classification ensemble methods, which predict with better accuracy by combining multiple classifiers, became a powerful machine learning and data mining paradigm. Well-known methodologies of classification ensemble are boosting, bagging and random forest. In this article, we assume that decision trees are used as classifiers in the ensemble. Further, we hypothesized that tree size affects classification accuracy. To study how the tree size in uences accuracy, we performed experiments using twenty-eight data sets. Then we compare the performances of ensemble algorithms; bagging, double-bagging, boosting and random forest, with different tree sizes in the experiment.

• Korean Journal of Environment and Ecology
• /
• v.27 no.4
• /
• pp.473-486
• /
• 2013

Vegetational structure and successional sere were investigated for Paryeongsan Zone in the Dadohaehaesang National Park incorporated in National Park in 2011. To do so, seventy-five plots($100m^2$) were set up and surveyed. The surveyed plots were divided into six groups according to the analysis of classification by TWINSPAN; (I) Quercus acutissima community, (II) Q. serrata-Carpinus tschonoskii var. tschonoskii community, (III) Pinus densiflora-Q. mongolica community, (IV) Q. variabilis community, (V) P. rigida-Q. variabilis-P. densiflora community, (VI) Chamaecyparis obtusa community. The results of vegetation structure analysis were. I, IICommunity, were expected that the deciduous oak trees with deciduous oak trees or Carpinus tschonoskii var. tschonoskii competing with oak trees would flourish in a deciduous broad-leaved forest. III, VCommunity, were expected that the P. densiflora and P. rigida competing with oak trees would flourish in a deciduous broad-leaved forest. IVCommunity, have expanded the influence of Q. variabilis, but understory will be developed next ecological succession by a high percentage of Machilus thunbergii in frequency of warm-temperate trees. VI Community, Chamaecyparis obtusa community were expected continue. This Chamaecyparis obtusa community is picked thinning Chamaecyparis obtusa as moving purpose of National Park, it will be inducement a plant vegetation succession to the natural forest. Frequency of warm-temperate trees in the Paryeongsan Zone of warm temperate climate zone was a total 9 species, Machilus thunbergii, Eurya japonica, Elaeagnus macrophylla, etc.

• Journal of Korean Society of Forest Science
• /
• v.111 no.2
• /
• pp.302-310
• /
• 2022

Here we aimed to classify the major coniferous tree species (Pinus densiflora, Pinus koraiensis, and Larix kaempferi) by tree measurement information and machine learning algorithms to establish an efficient forest management plan. We used national forest monitoring information amassed over nine years for the measurement information of trees, and random forest (RF), XGBoost (XGB), and light GBM (LGBM) as machine learning algorithms. We compared and evaluated the accuracy of the algorithm through performance evaluation using the accuracy, precision, recall, and F1 score of the algorithm. The RF algorithm had the highest performance evaluation score for all tree species, and highest scores for Pinus densiflora, with an accuracy of about 65%, a precision of about 72%, a recall of about 60%, and an F1 score of about 66%. The classification accuracy for the dominant trees was higher than about 80% in the crown classes, but that of the co-dominant trees, the intermediate trees, and the overtopper trees was evaluated as low. We consider that the results of this study can be used as reference data for decision-making in the selection of thinning trees for forest management.