Ensemble classification involves combining individually trained classifiers to yield more accurate prediction, compared with individual models. Ensemble techniques are very useful for improving the generalization ability of classifiers. The random subspace ensemble technique is a simple but effective method for constructing ensemble classifiers; it involves randomly drawing some of the features from each classifier in the ensemble. The instance selection technique involves selecting critical instances while deleting and removing irrelevant and noisy instances from the original dataset. The instance selection and random subspace methods are both well known in the field of data mining and have proven to be very effective in many applications. However, few studies have focused on integrating the instance selection and random subspace methods. Therefore, this study proposed a new hybrid ensemble model that integrates instance selection and random subspace techniques using genetic algorithms (GAs) to improve the performance of a random subspace ensemble model. GAs are used to select optimal (or near optimal) instances, which are used as input data for the random subspace ensemble model. The proposed model was applied to both Kaggle credit data and corporate credit data, and the results were compared with those of other models to investigate performance in terms of classification accuracy, levels of diversity, and average classification rates of base classifiers in the ensemble. The experimental results demonstrated that the proposed model outperformed other models including the single model, the instance selection model, and the original random subspace ensemble model.

경기도 수리산 군포시험림의 식물군집구조를 파악하기 위하여 64개의 조사구를 설정하고 식생조사를 실시하였다. 전체 64개 조사구는 DCA ordination분석에 의하여 리기테다소나무군집(군집 I), 소나무군집(군집 II), 소나무-신갈나무군집(군집 III), 굴참나무군집(군집 IV), 갈참나무군집(군집 V), 졸참나무군집(군집 VI), 서어나무군집(군집 VII)으로 분리되었다. 식물군집구조분석결과 천이예측은 명확하지 않았으나 군집 I, 군집 III은 참나무류군집으로 천이가 예측되었고 군집II, 군집IV, 군집V, 군집VI은 현 군집을 유지할 것으로 추정되어 기후극상으로의 변화는 예측할 수 없었으며, 군집 VII은 기후극상으로서 계속 안정된 상태를 유지할 것이다. Shannon의 종다양도는 0.7430~1.3025이었고 토양산도는 전지역이 pH 4.16~5.13이었다.

This study was to analyze changes of landuse and environmental value of cultivate land for eight years from 1999 to 2007 year in greenbelt area, Seoul. Greenbelt area decreased from 166.82 ㎢ in 1999 to 156.50 ㎢ in 2007 according to removal policy. Regarding landuse status in 2007, forest field area accounted for 64.16 %, dry paddy area 4.10 %, facilitated farming area 3.82 %, rice paddy area 2.95 % out of total greenbelt area. Cultivate land occupied wide spaces with dry paddy area, facilitated farming area, tree nursery in Seocho-gu, Gangnam-gu and Gangdong-gu. Changes of landuse were serious in Seocho-gu. The trend of changes of landuse for eight years is that rice paddy area was changed dry paddy area by laying the ground and dry paddy area was changed facilitated farming area for intensive agriculture. Rice paddy area could change without permission by laying the ground in below 50 cm height and it was changed to green houses due to increase in profit and modern policy of agriculture. It is nessary to monitor landuse regularly, improve regulation for change of landuse, compensate a property loss for maintaining environmental value in greenbelt area.