http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
무료 샘플 판촉의 확장효과 향상을 위한 기계학습 접근법
원하람,안현철 한국경영정보학회 2019 한국경영정보학회 학술대회논문집 Vol.2019 No.11
오늘날 기업은 포화된 시장에서 수익을 창출하기 위하여 다양한 마케팅 전략을 시행하고 있다. 판촉은 마케팅 전략의 하위 전략으로써 구매를 유도하기 위해 고객에게 인센티브를 제공하는 것을 말한다. 본 연구는 판촉방법 중 기업이 가장 쉽게 활용할 수 있는 무료샘플 판촉을 데이터 마이닝의 관점에서 접근하였다. 무료샘플 판촉 주된 목적 중 하나인 확장효과를 보인 고객을 판별하는 다양한 기계학습별 모델을 제안하였으며, 그 결과 XGBoost가 최적의 모델로 선정되었다. 또한 본 연구는 실무적 의의를 더하기 위해 확장효과를 보인 고객의 특성을 파악하였다.
A Recidivism Prediction Model Based on XGBoost Considering Asymmetric Error Costs
원하람,심재승,안현철 한국지능정보시스템학회 2019 지능정보연구 Vol.25 No.1
Recidivism prediction has been a subject of constant research by experts since the early 1970s. But it has become more important as committed crimes by recidivist steadily increase. Especially, in the 1990s, after the US and Canada adopted the 'Recidivism Risk Assessment Report' as a decisive criterion during trial and parole screening, research on recidivism prediction became more active. And in the same period, empirical studies on 'Recidivism Factors' were started even at Korea. Even though most recidivism prediction studies have so far focused on factors of recidivism or the accuracy of recidivism prediction, it is important to minimize the prediction misclassification cost, because recidivism prediction has an asymmetric error cost structure. In general, the cost of misrecognizing people who do not cause recidivism to cause recidivism is lower than the cost of incorrectly classifying people who would cause recidivism. Because the former increases only the additional monitoring costs, while the latter increases the amount of social, and economic costs. Therefore, in this paper, we propose an XGBoost(eXtream Gradient Boosting; XGB) based recidivism prediction model considering asymmetric error cost. In the first step of the model, XGB, being recognized as high performance ensemble method in the field of data mining, was applied. And the results of XGB were compared with various prediction models such as LOGIT(logistic regression analysis), DT(decision trees), ANN(artificial neural networks), and SVM(support vector machines). In the next step, the threshold is optimized to minimize the total misclassification cost, which is the weighted average of FNE(False Negative Error) and FPE(False Positive Error). To verify the usefulness of the model, the model was applied to a real recidivism prediction dataset. As a result, it was confirmed that the XGB model not only showed better prediction accuracy than other prediction models but also reduced the cost of misclassification most effectively.
원하람(Ha-Ram Won),심재승(Jae-Seung Shim),안현철(Hyunchul Ahn) 한국IT서비스학회 2018 한국IT서비스학회 학술대회 논문집 Vol.2018 No.-
범죄예측은 더 이상 공상과학 속 이야기가 아니라 정보통신기술과 빅데이터를 통하여 현실화되고 있다. 범죄예측 중에서도 범죄자의 재범 가능성을 예측하는 재범 가능성 예측은 학문적, 실무적 연구가 지속되어왔지만, 여전히 재범률은 높으며 각종 사회적 문제와 비용을 수반하고 있다. 이에 본 연구는 데이터 마이닝 기반의 자동화된 재범 가능성 예측모형을 제안한다. 특히 본 연구 에서는 재범 가능성 예측의 비대칭 오류비용 구조를 고려하여, 적절한 임계값 설정을 통해 재범 가능성 예측의 종합적인 비용을 줄이는 방안에 대해서도 모색한다.
온라인 무료 샘플 판촉의 효과적 활용을 위한 기계학습 기반 고객분류예측 모형
원하람 ( Won Ha-ram ),김무전 ( Kim Moo-jeon ),안현철 ( Ahn Hyunchul ) 한국정보시스템학회 2018 情報시스템硏究 Vol.27 No.3
Purpose The purpose of this study is to build a machine learning-based customer classification model to promote customer expansion effect of the free sample promotion. Specifically, the proposed model classifies potential target customers who are expected to purchase the products included in the free sample promotion after receiving the free samples. Design/methodology/approach This study proposes to build a customer classification model for determining customers suitable for providing free samples by using various machine learning techniques such as logistic regression, multiple discriminant analysis, case-based reasoning, decision tree, artificial neural network, and support vector machine. To validate the usefulness of the proposed model, we apply it to a real-world free sample-based target marketing case of a Korean major cosmetic retail company. Findings Experimental results show that a machine learning-based customer classification model presents satisfactory accuracy ranging from 70% to 75%. In particular, support vector machine is found to be the most effective machine learning technique for free sample-based target marketing model. Our study sheds a light on customer relationship management strategies using free sample promotions.
A Study on the Effect of the Document Summar ization Technique on the Fake News Detection Model
심재승,원하람,안현철 한국지능정보시스템학회 2019 지능정보연구 Vol.25 No.3
Fake news has emerged as a significant issue over the last few years, igniting discussions and research on how to solve this problem. In particular, studies on automated fact-checking and fake news detection using artificial intelligence and text analysis techniques have drawn attention. Fake news detection research entails a form of document classification; thus, document classification techniques have been widely used in this type of research. However, document summarization techniques have been inconspicuous in this field. At the same time, automatic news summarization services have become popular, and a recent study found that the use of news summarized through abstractive summarization has strengthened the predictive performance of fake news detection models. Therefore, the need to study the integration of document summarization technology in the domestic news data environment has become evident. In order to examine the effect of extractive summarization on the fake news detection model, we first summarized news articles through extractive summarization. Second, we created a summarized news-based detection model. Finally, we compared our model with the full-text-based detection model. The study found that BPN(Back Propagation Neural Network) and SVM(Support Vector Machine) did not exhibit a large difference in performance; however, for DT(Decision Tree), the full-text-based model demonstrated a somewhat better performance. In the case of LR(Logistic Regression), our model exhibited the superior performance. Nonetheless, the results did not show a statistically significant difference between our model and the full-text-based model. Therefore, when the summary is applied, at least the core information of the fake news is preserved, and the LR-based model can confirm the possibility of performance improvement. This study features an experimental application of extractive summarization in fake news detection research by employing various machine-learning algorithms. The study’s limitations are, essentially, the relatively small amount of data and the lack of comparison between various summarization technologies. Therefore, an in-depth analysis that applies various analytical techniques to a larger data volume would be helpful in the future.