A new case-deletion strategy for case-base maintenance based on K-means Clustering Algorithm applied to medical data

Case-Based Reasoning (CBR) system maintenance is an important issue for current medical systems research. Large-scale CBR systems are becoming more omnipresent, with immense case libraries consisting of millions of cases. Case-Base Maintenance (CBM) is the implementation of the following policies allowing to revise the organization and/or the content (information content, representation field of application, or the implementation) of the Case Base (CB) to improve future thinking. Diverse case-base deletion and addition policies have been proposed which claim to preserve case-base competence. This paper presents a novel clustering-based deletion policy for CBM that exploits the K-means clustering algorithm. Thus, CBM becomes a central subject whose objective is to guarantee the quality of the CB and improve the performance of CBM. The proposed approach exploited clustering, which groups similar cases using the K-means algorithm. We rely on the characterization made of the different cases in the CB, and we find this characterization by a method based on a criterion of competence and performance. From this categorization, case deletion becomes obvious. This quality depends on the competence and performance of the CB. Test results show that the proposed deletion strategy improved the efficiency of the CB while preserving competence.Furthermore, its performance was 13% more reliable. The effectiveness of the proposed approach examined on the medical databases and its performance has been compared with the existing approaches on deletion policy. Experimental results are very encouraging.

Deletion statistic accuracy in confirmatory factor models

Social science researchers now routinely use confirmatory factor models in scale development and validation studies. Methodologists have known for some time that the results of fitting a confirmatory factor model can be unduly influenced by one or a few cases in the data. However, there has been little development and use of case diagnostics for identifying influential cases with confirmatory factor models. A few case deletion statistics have been proposed to identify influential cases in confirmatory factor models. However, these statistics have not been systematically evaluated or compared for their accuracy. This study evaluated the accuracy of three case deletion statistics found in the R package influence.SEM. The accuracy of the case deletion statistics was also compared to Mahalanobis distance, which is commonly used to screen for unusual cases in multivariate applications. A statistical simulation was used to compare the accuracy of the statistics in identifying target cases generated from a model in which variables were uncorrelated. The results showed that Likelihood distance and generalized Cook’s distance detected the target cases more effectively than the Chi-square difference statistic. The accuracy of the Likelihood distance and generalized Cook’s distance statistics was unaffected by model misspecification. The results of this study suggest that Likelihood distance and generalized Cook’s distance are more accurate under more varied conditions in identifying target cases in confirmatory factor models.

DAMPAK REDENOMINASI TERHADAP INFLASI INDONESIA: PENANGANAN MISSING MENGGUNAKAN METODE CASE DELETION, PMM, RF DAN BAYESIAN

Indonesia is the country with the third largest currency digit after Vietnam and Zimbabwe. In 2010, Indonesia conveyed a discourse on the application of rupiah redenomination, but in its implementation it was necessary to estimate the economic factors that would be affected, especially inflation, where inflation was one of the decisive indicators of the success of the redenomination policy of the currency. To estimate the impact of redenomination on inflation, Indonesia can reflect on the historical data of countries that have implemented the policy. Based on historical data, a model can be applied to Indonesia. Historical data includes macroeconomic variables and forms of government. To get a model with better precision, complete data needs to be considered. The historical missing will make the inferencing obtained invalid and important information that can be used for analysis also diminishes. The case deletion method, mean matching predictive, random forest, and bayesian linear regression can be used to handle it. The results showed that there were 38.18% missing data from total observations and the case deletion method as the best method. Then the condition of hyperinflation, economic growth, and the index of government forms significantly impacted inflation after the implementation of redenomination. So, if Indonesia applies redenomination between the period 2010-2017, with the classification accuracy of 64.71%, it is estimated that it will have a negative impact because the inflation will increase after redenomination is implemented.

When Should We Ignore Examples with Missing Values?

In practice, the dataset collected from data mining usually contains some missing values. It is common practice to perform case deletion by ignoring those data with missing values if the missing rate is certainly small. The aim of this paper is to answer the following question: When should one directly ignore sampled data with missing values? By using different types of datasets having various numbers of attributes, data samples, and classes, it is found that there are some specific patterns that can be considered for case deletion over different datasets without significant performance degradation. In particular, these patterns are extracted to act as the decision rules by a decision tree model. In addition, a comparison is made between cases with deletion and imputation over different datasets with the allowed missing rates and the decision rules. The results show that the classification performance results obtained by case deletion and imputation are similar, which demonstrates the reliability of the extracted decision rules.

POLITENESS STRATEGIES AND LINGUISTIC POLITENESS MARKERS OF IMPERATIVE IN THE VERY BEST OF DONALD DUCK COMIC SERIES AND THEIR TRANSLATION IN INDONESIAN

<p>The present study examines politeness strategies and linguistic politeness markers of English imperative speech acts used in The Very Best of Donald Duck Comics Series. It also identifies the translation techniques applied to translate those markers into Indonesian and evaluate their accuracy and acceptability. The findings indicate that three politeness strategies (bald on record, positive politness and negative politeness) are used and about thirty five linguistic politeness markers are identified and translated in Indonesian using five translation techniques (literal, variation, deletion, borrowing and established equivalence). The findings also show that the accuracy and acceptability of the translation of linguistic politeness markers are found to be good. Nevertheless, the application of deletion technique tends to distract the pragmatic meaning and force of the linguistic politeness markers in the target language. In such a case, deletion technique should be avoided.</p><p> </p><p>Key words: linguistic politeness stretegies, linguistic politeness markers, translation technique, accuracy, acceptability</p>