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Extracting Rules from Neural Networks with Continuous Attributes
Batselem Jagvaral(바트셀렘),Wan-Gon Lee(이완곤),Myung-joong Jeon(전명중),Hyun-Kyu Park(박현규),Young-Tack Park(박영택 ) Korean Institute of Information Scientists and Eng 2018 정보과학회논문지 Vol.45 No.1
Over the decades, neural networks have been successfully used in numerous applications from speech recognition to image classification. However, these neural networks cannot explain their results and one needs to know how and why a specific conclusion was drawn. Most studies focus on extracting binary rules from neural networks, which is often impractical to do, since data sets used for machine learning applications contain continuous values. To fill the gap, this paper presents an algorithm to extract logic rules from a trained neural network for data with continuous attributes. It uses hyperplane-based linear classifiers to extract rules with numeric values from trained weights between input and hidden layers and then combines these classifiers with binary rules learned from hidden and output layers to form non-linear classification rules. Experiments with different datasets show that the proposed approach can accurately extract logical rules for data with nonlinear continuous attributes.
생활 패턴 인지를 위한 이벤트 연산 기반 예측 모델 학습 기법
배석현(Seok-Hyun Bae),방성혁(Sung-hyuk Bang),박현규(Hyun-Kyu Park),전명중(Myung-Joong Jeon),김제민(Je-Min Kim),박영택(Young-Tack Park) Korean Institute of Information Scientists and Eng 2018 정보과학회논문지 Vol.45 No.5
Several studies have been conducted on data analysis and predicting results with the advance of machine learning algorithms. Still, there are many problems of cleaning the noise of the real-life dataset, which is disturbing a clear recognition on complex patterns of human intention. To overcome this limitation, this paper proposes an event calculus methodology with 3 additional steps for the recognition of human intention: intention reasoning, conflict resolution, and noise reduction. Intention reasoning identifies the intention of the living pattern time-series data. In conflict resolution, existing ongoing intentions and inferred intention are checked by a conflict graph, so that the intentions that can occur in parallel are inferred. Finally, for noise reduction, the inferred intention from the noise of living pattern data is filtered by the history of fluent. For the evaluation of the event calculus module, this paper also proposes data generation methodology based on a gaussian mixture model and heuristic rules. The performance estimation was conducted with 300 sequential instances with 5 intentions that were observed for 13 hours. An accuracy of 89.3% was achieved between the probabilistic model and event calculus module.
최현영(Hyun-Young Choi),홍지훈(Ji-Hun Hong),이완곤(Wan-Gon Lee),바트셀렘(Batselem Jagvaral),전명중(Myung-Joong Jeon),박현규(Hyun-Kyu Park),박영택(Young-Tack Park) Korean Institute of Information Scientists and Eng 2018 정보과학회논문지 Vol.45 No.9
In recent years, a number of studies have been conducted for the purpose of automatically building a knowledge base that is based on web data. However, due to the incomplete nature of web data, there can be missing data or a lack of connections among the data entities that are present. In order to solve this problem, recent studies have proposed methods that train a model to predict this missing data through an artificial neural network based on natural language embedding, but there is a drawback to embedding entities. In practice, natural language corpus is not present in many knowledge bases. Therefore, in this paper, we propose a knowledge completion method that converts the knowledge base of RDF data into an RDF-sentence and uses embedding to create word vectors. We conducted a triple classification experiment in order to measure the performance of the proposed method. The proposed method was then compared with existing NTN models, and on average, 15% accuracy was obtained. In addition, we obtained 88%accuracy by applying the proposed method to the Korean knowledge base known as WiseKB.