• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.15-23
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• 2021

Most of the open-source decision tree algorithms are based on three splitting criteria (Entropy, Gini Index, and Gain Ratio). Therefore, the advantages and disadvantages of these three popular algorithms need to be studied more thoroughly. Comparisons of the three algorithms were mainly performed with respect to the predictive performance. In this work, we conducted a comparative experiment on the splitting criteria of three decision trees, focusing on their interpretability. Depth, homogeneity, coverage, lift, and stability were used as indicators for measuring interpretability. To measure the stability of decision trees, we present a measure of the stability of the root node and the stability of the dominating rules based on a measure of the similarity of trees. Based on 10 data collected from UCI and Kaggle, we compare the interpretability of DT (Decision Tree) algorithms based on three splitting criteria. The results show that the GR (Gain Ratio) branch-based DT algorithm performs well in terms of lift and homogeneity, while the GINI (Gini Index) and ENT (Entropy) branch-based DT algorithms performs well in terms of coverage. With respect to stability, considering both the similarity of the dominating rule or the similarity of the root node, the DT algorithm according to the ENT splitting criterion shows the best results.

• The Korean Journal of Nuclear Medicine
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• v.3 no.1
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• pp.83-99
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• 1969

There have been reported numberous cases of liver scanning in use of $^{198}Au$ colloid by many investigators, however, one in use of $^{113m}In$ colloid has not been reported as yet in this country. The dose of $^{113m}In$ for high diagnostic value in examination of each organ was determined and the dignostic interpretability of liver scanning with the use of $^{113m}In$ was carefully evaluated in comparison with the results of the liver scanning by the conventionally applied radioisotopes. The comparative study of both figures of liver scannings with the use of $^{113m}In$ colloid and $^{198}Au$ colloid delivered following results: 1. The liver uptake rate and clearance into peripheral blood were accentuated more in case of $^{113m}In$ colloid than in case of $^{198}Au$ colloid. 2. The interpretability of space occupying lesion in liver scanning with $^{113m}In$ was also superior to one with $^{198}Au$. 3. The figure of liver scanning with $^{113m}In$ colloid corresponds not always to the figure with $^{198}Au$. This difference can be explained by differences of phagocytic ability of reticuloendotherial system within liver. 4. In the liver scanning with $^{113m}In$ colloid, the spleen is also visualized even in normal examinee. 5. In the cases of disturbed liver function, uptake is more decreased in use of $^{113m}In$ colloid than in $^{198}Au$, in the spleen, however, the way is contrary. 6. With use of $^{113m}In$ colloid, the time required for scanning could be shortened in comparison with $^{198}Au$. 7. The filtration of $^{113m}In$ colloid for scanning prior to human administration gives an expectation for better scanning figure.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.731-738
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• 2011

In this paper, we introduce an advanced architecture of K-Means clustering-based polynomial Radial Basis Function Neural Networks (p-RBFNNs) designed with the aid of SSOA (Space Search Optimization Algorithm) and develop a comprehensive design methodology supporting their construction. In order to design the optimized p-RBFNNs, a center value of each receptive field is determined by running the K-Means clustering algorithm and then the center value and the width of the corresponding receptive field are optimized through SSOA. The connections (weights) of the proposed p-RBFNNs are of functional character and are realized by considering three types of polynomials. In addition, a WLSE (Weighted Least Square Estimation) is used to estimate the coefficients of polynomials (serving as functional connections of the network) of each node from output node. Therefore, a local learning capability and an interpretability of the proposed model are improved. The proposed model is illustrated with the use of nonlinear function, NOx called Machine Learning dataset. A comparative analysis reveals that the proposed model exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• The Korean Journal of Applied Statistics
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• v.26 no.1
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• pp.131-139
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• 2013

Support vector machines(SVM) may perform poorly in the presence of noise variables; in addition, it is difficult to identify the importance of each variable in the resulting classifier. A feature selection can improve the interpretability and the accuracy of SVM. Most existing studies concern feature selection in the linear SVM through penalty functions yielding sparse solutions. Note that one usually adopts nonlinear kernels for the accuracy of classification in practice. Hence feature selection is still desirable for nonlinear SVMs. In this paper, we compare the performances of nonlinear feature selection methods such as component selection and smoothing operator(COSSO) and kernel iterative feature extraction(KNIFE) on simulated and real data sets.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.4
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• pp.95-112
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• 2013

Information system (IS) security continues to present a challenge for firms. Especially, IT security accident is recently taking place successively in the financial sector. Thus, a comprehensive measure on this is demanded. A large part of a research on security relies upon technical design in nature and is restrictive in a consideration of person and organizational issue. To achieve a goal of firm security, it is possible with an effort of organizational management and supervision for maintaining the technical and procedural status. Based on a theory of accountability, we propose that the security countermeasures of organization lead to an increase in accountability and reduction in risk of IT security in a financial firm and further to firm performance like promotion in firm reliability. In addition, we investigate which difference a theoretical model shows by comparison between South Korean and American financial firms. As a result of analysis, it found that South Korea and America have significant difference, but that a measure on the financing IT security is important for both countries. We aim to enhance interpretability of a research on security by comparatively analysis between countries and conducting a study focus on specific firm called financial business. Our study suggest new theoretical framework to a research of security and provide guideline on design of security to financial firm.

• Korean Journal of Agricultural and Forest Meteorology
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• v.1 no.2
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• pp.142-150
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• 1999

During the past three decades after the first attempt to use satellite imagery or derived cloud products for rainfall estimation, much is known and understood concerning the scope and difficulties of satellite rainfall monitoring. After a brief general introduction this paper reviews recent progress in this field with special reference to improvement of algorithms, inter-comparison projects, integrative use of data from different sources, increasing lengths of data records and derived products, and interpretability of rainfall results. Also the paradigm of TRMM (Tropical Rainfall Measuring Mission) which is the first space mission(1997) dedicated to measuring tropical and subtropical rainfall though microwave and visible/infrared sensors, including the first spaceborne rain radar was introduced, and the potential applicability to the field of agriculture and water resources by combining satellite imagery is described.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.1
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• pp.184-192
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• 2011

In this study, we introduce a identification methodology for FCM-based fuzzy model. The two underlying design mechanisms of such networks involve Fuzzy C-Means (FCM) clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on FCM clustering method for efficient processing of data and the optimization of model was carried out using PSO. The premise part of fuzzy rules does not construct as any fixed membership functions such as triangular, gaussian, ellipsoidal because we build up the premise part of fuzzy rules using FCM. As a result, the proposed model can lead to the compact architecture of network. In this study, as the consequence part of fuzzy rules, we are able to use four types of polynomials such as simplified, linear, quadratic, modified quadratic. In addition, a Weighted Least Square Estimation to estimate the coefficients of polynomials, which are the consequent parts of fuzzy model, can decouple each fuzzy rule from the other fuzzy rules. Therefore, a local learning capability and an interpretability of the proposed fuzzy model are improved. Also, the parameters of the proposed fuzzy model such as a fuzzification coefficient of FCM clustering, the number of clusters of FCM clustering, and the polynomial type of the consequent part of fuzzy rules are adjusted using PSO. The proposed model is illustrated with the use of Automobile Miles per Gallon(MPG) and Boston housing called Machine Learning dataset. A comparative analysis reveals that the proposed FCM-based fuzzy model exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• The Journal of Bigdata
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• v.3 no.2
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• pp.101-112
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• 2018

The purpose of the study is to predict the number of police calls using neural network which is one of the machine learning and negative binomial regression, by using the data of 112 police calls received from Chungnam Provincial Police Agency from June 2016 to May 2017. The variables which may affect the police calls have been selected for developing the prediction model : time, holiday, the day before holiday, season, temperature, precipitation, wind speed, jurisdictional area, population, the number of foreigners, single house rate and other house rate. Some variables show positive correlation, and others negative one. The comparison of the methods can be summarized as follows. Neural network has correlation coefficient of 0.7702 between predicted and actual values with RMSE 2.557. Negative binomial regression on the other hand shows correlation coefficient of 0.7158 with RMSE 2.831. Neural network has low interpretability, but an excellent predictability compared with the negative binomial regression. Based on the prediction model, the police agency can do the optimal manpower allocation for given values in the selected variables.