• Journal of KIISE
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• v.44 no.6
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• pp.581-586
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• 2017

For automatic web scraping, unnecessary components such as menus and advertisements need to be removed from web pages and main contents should be extracted automatically. A content block tends to be located in the middle of a web page. In particular, Korean web documents rarely include metadata and have a complex design; a suitable method of content extraction is therefore needed. Existing content extraction algorithms use the textual and structural features of content blocks because processing visual features requires heavy computation for rendering and image processing. In this paper, we propose a new content extraction method using the tag positions in HTML as a quasi-visual feature. In addition, we develop a tag rank position, a type of tag position not affected by text length, and show that gradient boosting with the tag rank position is a very accurate content extraction method. The result of this paper shows that the content extraction method can be used to collect high-quality text data automatically from various web pages.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.574-576
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• 2022

최근 여러 분야에서 그래프 신경망(graph neural network, GNN)이 활발히 연구되고 있다. 하지만 지금까지 대부분의 GNN 연구는 단일 GNN 모델의 성능을 향상하는 데 집중되었다. 본 논문에서는 앙상블(ensemble) 기법의 대표적 기법인 그래디언트 부스팅(gradient boosting)을 이용하여 GNN의 앙상블 모델을 만드는 방법을 제안한다. 제안 방법은 앞서 만들어진 GNN의 오차를 경사 하강법(gradient descent)을 이용하여 감소시키는 방향으로 다음 GNN을 생성한다. 이 과정을 반복하여 GNN의 최종 앙상블 모델을 얻는다. 실험에서 GNN의 대표적인 모델인 그래프 합성곱 신경망(graph convolutional network, GCN)에 제안 방법을 적용하여 앙상블 모델을 생성한 결과, 단일 GCN 모델에 비해 노드 분류 정확도가 11.3%p까지 증가하였음을 확인하였다.

• Journal of Cadastre & Land InformatiX
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• v.46 no.2
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• pp.43-55
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• 2016

This study estimated land prices using instance-based learning. A k-nearest neighbor method was utilized among various instance-based learning methods, and the 10 distance metrics including Euclidean distance were calculated in k-nearest neighbor estimation. One distance metric prediction which shows the best predictive performance would be normally chosen as final estimate out of 10 distance metric predictions. In contrast to this practice, an ensemble technique which combines multiple predictions to obtain better performance was applied in this study. We applied the gradient boosting algorithm, a sort of residual-fitting model to our data in ensemble combining. Sales price data of farm lands in Haenam-gun, Jeolla Province were used to demonstrate advantages of instance-based learning as well as an ensemble technique. The result showed that the ensemble prediction was more accurate than previous 10 distance metric predictions.