• Journal of information and communication convergence engineering
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• 제5권3호
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• pp.233-238
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• 2007

An approach to identify Chinese event types is proposed in this paper which combines a good feature selection policy and a Maximum Entropy (ME) model. The approach not only effectively alleviates the problem that classifier performs poorly on the small and difficult types, but improve overall performance. Experiments on the ACE2005 corpus show that performance is satisfying with the 83.5% macro - average F measure. The main characters and ideas of the approach are: (1) Optimal feature set is built for each type according to local feature selection, which fully ensures the performance of each type. (2) Positive and negative features are explicitly discriminated and combined by using one - sided metrics, which makes use of both features' advantages. (3) Wrapper methods are used to search new features and evaluate the various feature subsets to obtain the optimal feature subset.

• International Journal of Computer Science & Network Security
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• 제21권5호
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• pp.129-132
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• 2021

Two feature selection approaches are analyzed in this study. First Approach used in this paper is Filter Approach which comprises of correlation technique. It provides two reduced feature sets using positive and negative correlation. Secondly Approach used in this paper is the wrapper approach which comprises of Sequential Forward Selection technique. The reduced feature set obtained by positive correlation results comprises of Rate of Acceleration, Intensity and Formant. The reduced feature set obtained by positive correlation results comprises of Rasta PLP, Log energy, Log power and Zero Crossing Rate. Pitch, Rate of Acceleration, Log Power, MFCC, LPCC is the reduced feature set yield as a result of Sequential Forwarding Selection.

• 한국조리학회지
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• 제22권8호
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• pp.109-121
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• 2016

This study analyzed the effect of five sub-variables' attribute of HMR: features of information, diversity, promptness, price and convenience, on the consumer purchasing intention. In addition, the regulation effect of positive reviews and negative reviews of consumers' online reviews between HMR selection attribute and purchasing intention was also tested. Results are following. First, convenience feature (B=.577, p<.001) and diversity feature (B=.093, p<.01) among the effect of HMR selection attribute had a positive (+) effect on purchasing intention. On the other hand, promptness feature (B=.235, p<.001) and price feature (B=.161, p<.001), and information feature (B=.288, p<.001) were not significant effect on purchasing intention. Second, result of regulation effect of the positive reviews of consumer's online review between the selection attribute of the HMR product and consumers' purchasing intention, in the first-stage model in which the selection attribute of the HMR product is input as an independent variable, there was a significant positive (+) effect on all the features of convenience, diversity, promptness, price, and information. In addition, there was significant positive (+) main effect (B=.472, p<.001) in the second step model in which the consumers' positive reviews, that is a regulation variable. Furthermore, the feature of price (B=.068, p<.05) had a significant positive (+) effect in the third stage in which the selection attribute of the HMR product that is an independent variable and the interaction of the positive review. However, the feature of information (B=-.063, p<.05) showed negative (-) effect, and there was no effect on the features of convenience, diversity, and promptness. Third, as a result of testing the regulation effect of the negative reviews of consumers' online reviews between HMR product selection attribute and consumers' purchasing intention, in the first-stage model in which the selection attribute of the HMR product was a positive (+) effect on all the features of convenience, diversity, promptness, price, and information. In the second-stage model in which consumers' negative reviews (B=-.113, p<.001) had negative (-) effect. In the third-stage in which the selection attribute of the HMR product and the interactions of the negative reviews was a positive (+) effect with the feature of price (B=.113, p<.01). Last, there was no effect at all on the features of convenience, promptness, and information.

• Journal of Information Processing Systems
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• 제2권1호
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• pp.23-27
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• 2006

This paper presents a feature selection technique based on distributional differences for efficient machine learning. Initial training data consists of data including many features and a target value. We classified them into positive and negative data based on the target value. We then divided the range of the feature values into 10 intervals and calculated the distribution of the intervals in each positive and negative data. Then, we selected the features and the intervals of the features for which the distributional differences are over a certain threshold. Using the selected intervals and features, we could obtain the reduced training data. In the experiments, we will show that the reduced training data can reduce the training time of the neural network by about 40%, and we can obtain more profit on simulated stock trading using the trained functions as well.

• 한국콘텐츠학회논문지
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• 제8권10호
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• pp.114-128
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• 2008

최근 몇 년 동안 브랜드 커뮤니티에 대한 연구는 많은 반면에, UCC에 관련된 연구는 아직 미비한 실정이며, 또한 브랜드 커뮤니티에서의 UCC의 연구도 활발하게 진행되고 있지 못하다. 본 연구에서는 UCC를 하나의 새로운 현상으로 보는 것이 아니라 이러한 UCC가 브랜드커뮤니티에서 상호작용을 통해서 퍼뮤니케이션되는 현상을 학문적 연구를 통해 시사점을 제시하고 브랜드 커뮤니티에 관한 향후 연구의 방향을 제시하는데 목적이 있다. 이에 따라서 본 연구는 온라인 브랜드 커뮤니티에서의 UCC를 통한 상호작용이 펌 행위의도와 방문충성도에 미치는 영향에 대해 규명하고자 하였다. 연구결과를 살펴보면, 첫째, 브랜드 커뮤니티의 품질특성 중에서 정보성만이 모든 상호작용성에 정의 영향을 미치는 것을 알 수 있다. 둘째, 브랜드 커뮤니티의 신뢰 및 응답성 부분은 상호작용의 구성요인 중에서 연결성 부분에만 정의 영향을 미치는 것으로 나타났다. 셋째, 확신성은 상호작용요인 중에서 능동성과 연결성에만 정의 영향을 미치는 것을 알 수 있다. 다섯째, 공유의지가 상호작용의 구성요인 중에서 능동성과 연결성에는 영향을 미치고 있지만 반응성에는 영향을 미치지 못하는 것으로 나타났으며, 마지막으로 상호작용성이 펌 행위의도에 반응성 부분에만 영향을 미치지 못하였고, 방문충성도에는 모두 긍정적인 영향을 미쳤다. 앞에서 살펴본 내용을 바탕으로 브랜드 커뮤니티의 품질특성과 사용자특성요인 중에서 상호작용성에 많은 영향을 미치는 변수로 정보성, 확신성 그리고 사용자 특성요인인 공유의지를 들 수 있다.

• 전자공학회논문지
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• 제49권12호
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• pp.124-130
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• 2012

본 논문에서는 현재 진행 중인 MPEG(Motion Picture Experts Group, ISO/IEC JTC1 SC29 WG11)의 표준화 작업 중 CDVS(Compact Descriptor for Visual Search)의 CE-7(Core Experiment)인 특징점 선택에 대한 방법을 제안한다. 서술자의 경량화를 위해서는 영상으로부터 추출된 많은 수의 특징점들 중에서 영상 정합에 사용될 중요한 특징점들을 선택해야 한다. 본 논문에서는 최 인접 거리 비율 정합(Nearest Neighbor distance ratio matching) 방법에 의해 영상 정합 단계에서 사용되지 않고 버려지는 특징점들을 미리 추출 단에서 제거하는 방법 제안하였다. 제안된 방법을 통하여 적은 비트 전송률을 요하는 시스템에서 특징점의 낭비를 피할 수 있고 결과적으로 추가적인 특징점을 사용할 수 있으므로 전체적인 성능 향상을 얻을 수 있었다. 제안된 알고리즘을 통하여 Pair-wise 정합 실험에서 기존의 Test Model 대비 최고 2.3%의 성공율(True positive rate)의 향상을 보였다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2016년도 추계학술발표대회
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• pp.525-526
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• 2016

machine learning 기법 중 하나인 logistic regression을 이용하여 benign sample과 breast cancer sample을 구분할 수 있는데, 이 연구를 통해 classification의 정확도를 높이고 false positive와 false negative의 비율을 줄이려고 했다. 그래서 logistic regression의 parameter 값을 바탕으로 regression function에 영향을 많이 주는 feature 들을 선택하고, 영향력 있는 feature 들을 더한 새로운 feature를 추가했다. 그 결과 정확도와 F-score가 증가했으며, false positive, false negative의 비율이 감소했다.

• 융복합기술연구소 논문집
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• 제4권2호
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• pp.1-9
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• 2014

This paper examines feature-based reconstruction algorithm using feature-based modeling and based on topology optimization technology, which aims to achieve a minimal volume weight and to satisfy user-defined constraints such as stress, deformation related conditions. The finite element model after topology optimization allows us to remove some region of a solid model for predefined volume requirement. The stress or deformation distribution resulted from finite element analysis enables us to add some material to the solid model for a robust structure. For this purpose, we propose a feature-based redesign algorithm which inserts negative features to the solid model for material removal and positive features for material addition, and we introduce a bisection method which searches an optimal structure by iteratively applying the feature-based redesign algorithm. Several examples are considered to illustrate the proposed algorithms and to demonstrate the effectiveness of the present approach.

• 한국컴퓨터정보학회논문지
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• 제24권2호
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• pp.171-177
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• 2019

This paper aims to find the most effective feature selection method for the sake of opinion mining tasks. Basically, opinion mining tasks belong to sentiment analysis, which is to categorize opinions of the online texts into positive and negative from a text mining point of view. By using the five product groups dataset such as apparel, books, DVDs, electronics, and kitchen, TF-IDF and Bag-of-Words(BOW) fare calculated to form the product review feature sets. Next, we applied the feature selection methods to see which method reveals most robust results. The results show that the stacking classifier based on those features out of applying Information Gain feature selection method yields best result.

• 한국CDE학회논문집
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• 제9권1호
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• pp.51-61
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• 2004

Mechanical parts are often grouped into part families based on the similarity of their shapes, to support efficient manufacturing process planning and design modification. The 2-part sequence papers present similarity assessment techniques to support part family classification for machined parts. These exploit the multiple feature decompositions obtained by the feature recognition method using convex decomposition. Convex decomposition provides a hierarchical volumetric representation of a part, organized in an outside-in hierarchy. It provides local accessibility directions, which supports abstract and qualitative similarity assessment. It is converted to a Form Feature Decomposition (FFD), which represents a part using form features intrinsic to the shape of the part. This supports abstract and qualitative similarity assessment using positive feature volumes.. FFD is converted to Negative Feature Decomposition (NFD), which represents a part as a base component and negative machining features. This supports a detailed, quantitative similarity assessment technique that measures the similarity between machined parts and associated machining processes implied by two parts' NFDs. Features of the NFD are organized into branch groups to capture the NFD hierarchy and feature interrelations. Branch groups of two parts' NFDs are matched to obtain pairs, and then features within each pair of branch groups are compared, exploiting feature type, size, machining direction, and other information relevant to machining processes. This paper, the second one of the two companion papers, describes the similarity assessment method using NFD.