• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.

• MISULJARYO - National Museum of Korea Art Journal
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• v.98
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• pp.104-129
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• 2020

Designated as a national treasure of Korea and inscribed on the UNESCO World Heritage List, the Tripitaka Koreana at Haeinsa Temple is the world's oldest and most comprehensive extant version of the Tripitaka in Hanja script (i.e., Chinese characters). The set consists of 81,352 carved woodblocks, some of which have two or more copies, which are known as "duplicate woodblocks." These duplicates are supplementary woodblocks (bogakpan) that were carved some time after the original production, likely to replace blocks that had been eroded or damaged by repeated printings. According to the most recent survey, the number of supplementary woodblocks is 118, or approximately 0.14% of the total set, which attests to the outstanding preservation of the original woodblocks. Research on the supplementary woodblocks can reveal important details about the preservation and management of the Tripitaka Koreana woodblocks. Most of the supplementary woodblocks were carved during the Joseon period (1392-1910) or Japanese colonial period (1910-1945). Although the details of the woodblocks from the Japanese colonial period have been recorded and organized to a certain extent, no such efforts have been made with regards to the woodblocks from the Joseon period. This paper analyzes the characteristics and production date of the supplementary woodblocks of the Tripitaka Koreana. The sutra with the most supplementary woodblocks is the Maha Prajnaparamita Sutra (Perfection of Transcendental Wisdom), often known as the Heart Sutra. In fact, 76 of the total 118 supplementary woodblocks (64.4%) are for this sutra. Hence, analyses of printed versions of the Maha Prajnaparamita Sutra should illuminate trends in the carving of supplementary woodblocks for the Tripitaka Koreana, including the representative characteristics of different periods. According to analysis of the 76 supplementary woodblocks of the Maha Prajnaparamita Sutra, 23 were carved during the Japanese colonial period: 12 in 1915 and 11 in 1937. The remaining 53 were carved during the Joseon period at three separate times. First, 14 of the woodblocks bear the inscription "carved in the mujin year by Haeji" ("戊辰年更刻海志"). Here, the "mujin year" is estimated to correspond to 1448, or the thirtieth year of the reign of King Sejong. On many of these 14 woodblocks, the name of the person who did the carving is engraved outside the border. One of these names is Seonggyeong, an artisan who is known to have been active in 1446, thus supporting the conclusion that the mujin year corresponds to 1448. The vertical length of these woodblocks (inside the border) is 21 cm, which is about 1 cm shorter than the original woodblocks. Some of these blocks were carved in the Zhao Mengfu script. Distinguishing features include the appearance of faint lines on some plates, and the rough finish of the bottoms. The second group of supplementary woodblocks was carved shortly after 1865, when the monks Namho Yeonggi and Haemyeong Jangung had two copies of the Tripitaka Koreana printed. At the time, some of the pages could not be printed because the original woodblocks were damaged. This is confirmed by the missing pages of the extant copy that is now preserved at Woljeongsa Temple. As a result, the supplementary woodblocks are estimated to have been produced immediately after the printing. Evidently, however, not all of the damaged woodblocks could be replaced at this time, as only six woodblocks (comprising eight pages) were carved. On the 1865 woodblocks, lines can be seen between the columns, no red paint was applied, and the prayers of patrons were also carved into the plates. The third carving of supplementary woodblocks occurred just before 1899, when the imperial court of the Korean Empire sponsored a new printing of the Tripitaka Koreana. Government officials who were dispatched to supervise the printing likely inspected the existing blocks and ordered supplementary woodblocks to be carved to replace those that were damaged. A total of 33 supplementary woodblocks (comprising 56 pages) were carved at this time, accounting for the largest number of supplementary woodblocks for the Maha Prajnaparamita Sutra. On the 1899 supplementary woodblocks, red paint was applied to each plate and one line was left blank at both ends.