• Journal of the Korean Data and Information Science Society
• /
• v.22 no.2
• /
• pp.235-243
• /
• 2011

The most widely used data mining technique is to explore association rules. This technique has been used to find the relationship between each set of items based on the association thresholds such as support, confidence, lift, etc. There are many interestingness measures as the criteria for evaluating association rules. Among them, confidence is the most frequently used, but it has the drawback that it can not determine the direction of the association. The net confidence measure was developed to compensate for this drawback, but it is useless in the case that the value of positive confidence is the same as that of negative confidence. This paper propose a attributably pure confidence to evaluate association rules and then describe some properties for a proposed measure. The comparative studies with confidence, net confidence, and attributably pure confidence are shown by numerical example. The results show that the attributably pure confidence is better than confidence or net confidence.

• Journal of the Korean Statistical Society
• /
• v.24 no.1
• /
• pp.183-195
• /
• 1995

For a distribution on the unit sphere, the set of eigenvectors of the second moment matrix is a conventional measure of orientation. Asymptotic confidence cones for eigenvector under the parametric assumptions for the underlying distributions and nonparametric confidence cones for eigenvector based on bootstrapping were proposed. In this paper, to reduce the level error of confidence cones for eigenvector, double bootstrap confidence cones based on prepivoting are considered, and the consistency of this method is discussed. We compare the perfomances of double bootstrap method with the others by Monte Carlo simulations.

• The Journal of Information Systems
• /
• v.20 no.3
• /
• pp.63-79
• /
• 2011

Deducting the risk level of infrastructure and buildings based on past human disaster risk cases and implementing prevention measures are important activities for disaster prevention. The object of this study is to measure the confidence to proceed quantitative analysis of various disaster risk cases through text mining methodology. Indeed, by examining confidence calculation process and method, this study suggests also a basic quantitative framework. The framework to measure the confidence is composed into four stages. First step describes correlation by categorizing basic elements based on human disaster ontology. Secondly, terms and cases of Term-Document Matrix will be created and the frequency of certain cases and terms will be quantified, the correlation value will be added to the missing values. In the third stage, association rules will be created according to the basic elements of human disaster risk cases. Lastly, the confidence value of disaster risk cases will be measured through association rules. This kind of confidence value will become a key element when deciding a risk level of a new disaster risk, followed up by preventive measures. Through collection of human disaster risk cases related to road infrastructure, this study will demonstrate a case where the four steps of the quantitative framework and process had been actually used for verification.

• The Journal of the Acoustical Society of Korea
• /
• v.22 no.3
• /
• pp.194-201
• /
• 2003

In speech recognition, confidence measuring is to decide whether it can be accepted as the recognized results or not. The confidence is measured by integrating frames into phone and word level. In case of word recognition, the confidence measuring verifies the results of recognition and Out-Of-Vocabulary (OOV). Therefore, the post-processing could improve the performance of recognizer without accepting it as a recognition error. In this paper, we measure the confidence modifying log likelihood ratio (LLR) which was the previous confidence measuring. It accumulates only those which the log likelihood ratio is negative when integrating the confidence to phone level from frame level. When comparing the verification performance for the results of word recognizer with the previous method, the FAR (False Acceptance Ratio) is decreased about 3.49% for the OOV and 15.25% for the recognition error when CAR (Correct Acceptance Ratio) is about 90%.

• Speech Sciences
• /
• v.12 no.1
• /
• pp.123-134
• /
• 2005

Confidence measure (CM) is used for the rejection of mis-recognized words in an automatic speech recognition (ASR) system. Rahim, Lee, Juang and Cho's confidence measure (RLJC-CM) is one of the widely-used CMs [1]. The RLJC-CM is calculated by averaging phone-level CMs. An extension of the RLJC-CM was achieved by Kim et al [2]. They devised the normalized CM (NCM), which is a statistically normalized version of the RLJC-CM by using the tri-phone based CM normalization. In this paper we verify the NCM by generalizing tri-phone to n-phone unit. To apply various units for the normalization, mono-phone, tri-phone, quin-phone and $\infty$-phone are tested. By the experiments in the domain of the isolated word recognition we show that tri-phone based normalization is sufficient enough to enhance the rejection performance of the ASR system. Also we explain the NCM in regard to two class pattern classification problems.

• Journal of the Korean Data and Information Science Society
• /
• v.19 no.4
• /
• pp.1269-1279
• /
• 2008

Data mining is the technique to find useful information in huge databases. One of the well-studied problems in data mining is exploration for association rules. An association rule technique finds the relation among each items in massive volume databases by several interestingness measures. An important and useful classification scheme of interestingness measures may be based on user-involvement. This results in two categories - objective and subjective measures. This paper present some relative interestingess measures to compare with degrees of association for two groups. A comparative study with some relative interestingness measures is shown by numerical example. The results show that the relative net confidence is the best relative interestingness measure.

• Journal of the Korean Data and Information Science Society
• /
• v.23 no.5
• /
• pp.939-948
• /
• 2012

The most widely used data mining technique is to explore association rules. This technique has been used to find the relationship between items in a massive database based on the interestingness measures such as support, confidence, lift, etc. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control.In general, association rule technique generates the rule, 'If A, then B.', whereas negative association rule technique generates the rule, 'If A, then not B.', or 'If not A, then B.'. We can determine whether we promote other products in addition to promote its products only if we add negative association rules to existing association rules. In this paper, we proposed the negatively attributable and pure confidence to overcome the problems faced by negative association rule technique, and then we checked three conditions for interestingness measure. The comparative studies with negative confidence, negatively pure confidence, and negatively attributable and pure confidence are shown by numerical examples. The results show that the negatively attributable and pure confidence is better than negative confidence and negatively pure confidence.

• ETRI Journal
• /
• v.27 no.4
• /
• pp.461-464
• /
• 2005

Recently, a variety of confidence measures for utterance verification has been studied to improve speech recognition performance by rejecting out-of-vocabulary inputs. Most of the conventional confidence measures for utterance verification are based primarily on hypothesis testing or an approximated posterior probability, and their performances depend on the robustness of an alternative hypothesis or the prior probability. We introduce a novel confidence measure called a search confusion rate (SCR), which does not require an alternative hypothesis or the approximation of posterior probability. Our confusion-based approach shows better performance in additive noise-corrupted speech as well as in clean speech.

• KIPS Transactions on Software and Data Engineering
• /
• v.4 no.7
• /
• pp.291-296
• /
• 2015

In this paper, we propose a new confidence measure using pixel selection for eye detection and design a hybrid eye detector. For this, we produce sub-images by applying a pixel selection method to the eye patches and construct the BDA(Biased Discriminant Analysis) feature space for measuring the confidence of the eye detection results. For a hybrid eye detector, we select HFED(Haar-like Feature based Eye Detector) and MFED(MCT Feature based Eye Detector), which are complementary to each other, as basic detectors. For a given image, each basic detector conducts eye detection and the confidence of each result is estimated in the BDA feature space by calculating the distances between the produced eye patches and the mean of positive samples in the training set. Then, the result with higher confidence is adopted as the final eye detection result and is used to the face alignment process for face recognition. The experimental results for various face databases show that the proposed method performs more accurate eye detection and consequently results in better face recognition performance compared with other methods.

• Korean Journal of Adult Nursing
• /
• v.22 no.4
• /
• pp.430-437
• /
• 2010

Purpose: The purpose of this study was to investigate the balance confidence and balance ability among community-residing stroke patients. Methods: The sample for this study was a convenience sample obtained from patients who visited welfare centers for the disabled in three cities from October to December in 2009. The Activities-specific Balance Confidence Scale and Berg Balance Scale were used to measure confidence in task performance and balance ability. Results: The level of balance confidence and balance ability were low in these subjects. There was a significant relationship between balance confidence and balance ability (r=.424, p=.000). Subjects with less balance ability felt less balance confidence. Conclusion: Among community-residing stroke patients, falls are common. Falls prevention which may contribute to the incidence of fall and fall related injury should be part of stroke rehabilitation plan. Also, it is necessary that an intervention for falls prevention after stroke should take into account factors such as the balance confidence and balance ability.