Combining Multiple Classifiers based on Dependency and its Application

다수 인식기를 결합하는 여러가지 방법들이 제안되었으나, 대부분이 다수 인식기를 결합하는데 있이서 인식기 간의 의존관계를 고려하지 않았다. 이것은 의존관계가 매우 높은 인식기가 추가될 때 다수 인식기를 결합하는 방법의 인식 성능을 저하시키거나 결합된 결과가 판향되도록 할 수도 있다. 본 논문에서는 이러한 단점을 극복하고 안정적인 인식 성능을 얻기 위하여 의존관계를 기반으로 다수 인식기를 결합하는 방법을 제안한다. 다수 인식기의 인식 결과로부터 의존관계를 결정하기 위하여 1차 의존관계로 근사하였으며, 두 가지 방법을 사용하였다. 하나는 상호 정보의 개념을 사용하는 것이고, 다른 하나는 통계적으로 측정된 결합도의 개념을 사용하는 것이다. 최적으로 결정된 1차 의존관계는 베이지안 공식을 사용하여 다수 인식기의 인식 결과를 결합하는데 사용된다. 무제약 온라인 숫자, 영문 알파벳 인식을 위한 문자 인식기를 사용하였다. 실험한 결과, 다수 문자 인식기를 결합한 인식 성능이 대체로 개별 문자 인식기의 성능보다 우수하였으며, 특히 의존관계가 매우 높은 문자 인식기가 추가되었을 때 의존관계를 기반으로 결합하는 방법이 다른 방법보다 더 우수한 성능을 보여 주었다

의존관계를 기반으로한 다수 결정의 결합

학위논문(박사) - 한국과학기술원 : 전산학과, 1997.8, [ [vii], 97 p. ]In order to overcome difficulties in improving classification performance using only a single classifier, the idea of combining multiple classifiers, as an alternative, has emerged from an assumption that two heads are better than one. The main task of combining multiple classifiers in parallel is how to combine their decisions (i.e., classification results). Although many decision combination methods have been proposed for combining multiple classifiers, most of them have not focused on dependencies among classifiers. They mainly combined multiple decisions on the basis of an independence assumption. Therefore, the performance of combining multiple classifiers tends to be degraded and biased, in case of adding highly dependent classifiers. Huang and Suen proposed Behavior-Knowledge Space (BKS) method, as an advanced study, which would no longer require the independence assumption. However, it is well known that for an application of the BKS method to the combination of K classifiers, storing and estimating a (K+1) st-order probability distribution composed of a decision variable and K decisions is exponentially complex and is unmanageable in theoretical analysis even for small K. Therefore, an approximation scheme is needed. To overcome such weaknesses and obtain robust performance, it is desirable that combining multiple classifiers be performed in a probabilistic way based on the product approximation of the (K+1)st-order probability distribution, without the independence assumption. Chow and Liu as well as Lewis proposed the approximation scheme of a high order probability distribution with a product of second-order distributions considering first-order tree dependency. In their approximation scheme, a dependency provides a theoretical basis on the identification of an optimal approximation of the high order probability distribution, using the measure of closeness proposed by Lewis. However, often we face cases in which a decision is based more than two ot...한국과학기술원 : 전산학과

CPUE Standardization Considering Spatio-temporal and Environmental Variables of Chub Mackerel Scomber japonicus in Korean Waters

The chub mackerel Scomber japonicus is the most important commercial species caught primarily by large purse seine fisheries. The effective management of chub mackerel resources requires a thorough understanding of the current stock status and the factors driving its fluctuations. The catch per unit effort (CPUE) is a crucial index representing the relative abundance of resources, and CPUE standardization was applied using a generalized linear model and generalized linear mixed model (GLMM). This study adopted various explanatory variables including spatiotemporal factors of Year, Month and Area (spatial clustering), and environmental factors of seawater temperature at a depth 50 m ((T50) and Tsushima Warm Current transport (TWC) and catch ratio of chub mackerel (Ratio). The GLMM, which incorporates random effects, was identified as the optimal model. Ratio had the most significant effect on the CPUE, and environmental and spatio-temporal factors had significant influences. Although the nominal CPUE showed an increasing trend across different areas, the standardized CPUE either decreased or exhibited a decreasing rate of increase. These findings serve as fundamental data for stock assessment and contribute to the spatiotemporal and environmentally informed management of chub mackerel resources in Korean waters.22Nkc