Using conservative estimation for conditional probability instead of ignoring infrequent case

Kikuchi, Masato; Yamamoto, Etsuhide; Yoshida, Mitsuo; Okabe, Masayuki; Umemura, Kyoji

doi:10.1109/icaicta.2016.7803098

Cited by 4 publications

(8 citation statements)

References 14 publications

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“…Kikuchi et al [9] propose to use the confidence interval by assuming that the prior distribution is a uniform distribution. They also contain one parameter to tune, which is the confidence level.…”

Section: Discussionmentioning

confidence: 99%

“…At each rank of the output, we compute the recall rate by a given value of rank. This metrics is also reference [9].…”

Section: Experimental Setting and Evaluation Methodsmentioning

confidence: 99%

“…There is another approach for this task. Kikuchi et al [9] argue that the cost where false association rules are selected is considerably higher than the cost where true association rules are not selected. Rather than estimating the true value, they proposed to form a confidence interval of estimation of the strength, and utilize the lower boundary of the interval in practice.…”

Section: Related Workmentioning

confidence: 99%

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Finding association rules by direct estimation of likelihood ratios

Kawakami

Kikuchi

Yoshida

et al. 2017

2017 International Conference on Advanced Informatics, Concepts, Theory, and Applications (ICAICTA)

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In this paper, we propose a cost function that corresponds to the mean square errors between estimated values and true values of conditional probability in a discrete distribution. We then obtain the values that minimize the cost function. This minimization approach can be regarded as the direct estimation of likelihood ratios because the estimation of conditional probability can be regarded as the estimation of likelihood ratio by the definition of conditional probability. When we use the estimated value as the strength of association rules for data mining, we find that it outperforms a well-used method called Apriori.

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Section: Discussionmentioning

confidence: 99%

“…At each rank of the output, we compute the recall rate by a given value of rank. This metrics is also reference [9].…”

Section: Experimental Setting and Evaluation Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Finding association rules by direct estimation of likelihood ratios

Kawakami

Kikuchi

Yoshida

et al. 2017

2017 International Conference on Advanced Informatics, Concepts, Theory, and Applications (ICAICTA)

Self Cite

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“…Therefore, it is important to measure interestingness appropriately for successful mining. In this section, we first introduce existing measures [1,4,6] and describe the problems of using them in our mining. Subsequently, we propose the measure WCC to mitigate the problems.…”

Section: Interestingness Measures For Association Rulesmentioning

confidence: 99%

“…In comparison, the proposed measure treats the two types of evidence with different weights. Moreover, our measure uses conservative confidence [4], which underestimates the interestingness of a rule if it has a low frequency. This allows dealing with low-frequency rules, which are ignored in general mining problems.…”

Section: Introductionmentioning

confidence: 99%

Matching Social Issues to Technologies for Civic Tech by Association Rule Mining using Weighted Casual Confidence

Kikuchi,

Shiramatsu,

Kozakai

et al. 2021

Preprint

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More than 80 civic tech communities in Japan are developing information technology (IT) systems to solve their regional issues. Collaboration among such communities across different regions assists in solving their problems because some groups have limited IT knowledge and experience for this purpose. Our objective is to realize a civic tech matchmaking system to assist such communities in finding better partners with IT experience in their issues. In this study, as the first step toward collaboration, we acquire relevant social issues and information technologies by association rule mining. To meet our challenge, we supply a questionnaire to members of civic tech communities and obtain answers on their faced issues and their available technologies. Subsequently, we match the relevant issues and technologies from the answers. However, most of the issues and technologies in this questionnaire data are infrequent, and there is a significant bias in their occurrence. Here, it is difficult to extract truly relevant issues-technologies combinations with existing interestingness measures. Therefore, we introduce a new measure called weighted casual confidence, and show that our measure is effective for mining relevant issues-technologies pairs.

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