Binary Document Classification Based on Fast Flux Discriminant with Similarity Measure on Word Set

Keisuke Okubo,Gendo Kumoi,Masayuki Goto 대한산업공학회 2019 Industrial Engineeering & Management Systems Vol.18 No.2

Fast Flux Discriminant (FFD) is known as one of the high-performance nonlinear binary classifiers, and it is possible to construct a classification model considering the interaction between variables. In order to take account of the interaction between variables, FFD introduces the histogram-based kernel smoothing using subspaces including variable combinations. However, when creating a subspace, the original FFD should cover all variables including combinations of variables with low interaction. Therefore, the disadvantage is that the calculation amount increases exponentially as the dimension increases. In this study, we calculate the similarity between variables by using KL divergence. Then, among the obtained similarities, divisions are performed for each subspace with similar variables. Through this method, we try to reduce the amount of calculation while maintaining classification accuracy by using only combinations of variables that are likely to take high interaction. Through the simulation experiments with Japanese newspaper articles, the effectiveness of our proposed method is clarified.

A Study on Customer Purchase Behavior Analysis Based on Hidden Topic Markov Models

Mio Hotoda,Gendo Kumoi,Masayuki Goto 대한산업공학회 2021 Industrial Engineeering & Management Systems Vol.20 No.1

Along with recent developments of Internet society, purchasing actions on E-commerce (hereinafter called “EC”) sites have become common for many consumers. On the other hand, it is known that the conversion rate (hereinafter called “CVR”) on EC sites is usually several percent at most. Therefore, many EC sites desire effective measures to improve CVR. In general, a user browses several pages on an EC site before he/she decide to purchase an item and it is considered that users’ intentions are reflected in their page transition tendency on an EC site. If a model analyzing the page transition data can extract users’ purchasing intentions, it enables to utilize the information for making a good promotion measure. Here, it is sometimes better to assume latent classes behind the users’ page transitions to understand their purchase intentions, because there are usually not only several user groups with different preferences but also plural states of purchasing intentions. However, previous models either assume the same latent topic on each page in the same session or assume a latent topic for each page every time. These models cannot handle situations where users’ intentions may change during browsing but not change frequently from page to page. In this study, we propose a purchasing behavior analysis model based on Hidden Topic Markov Models (HTMM). The proposed method can divide users’ browsing sequence into multiple subsequences with the same statistical characteristics according to latent topics estimated from page transitions. Then, the purchase probability of each latent topic can be obtained by using the purchase results obtained from the actual browsing history data together. By the proposed model, the purchase probabilities become possible to estimate the purchase intention of the users in real time and the information is effective for considering marketing measures. In this study, an experiment using real browsing history data is carried out and the effectiveness of the proposed method is demonstrated.

Deep Learning with Data Augmentation to Add Data Around Classification Boundaries

Hideki Fujinami,Gendo Kumoi,Masayuki Goto 대한산업공학회 2021 Industrial Engineeering & Management Systems Vol.20 No.3

Data augmentation methods are used as a technique to improve generalization by increasing the number of training data in image classification. However, most of these methods are not a data driven algorithm, the degree of improvement of generalization ability by performing these data augmentation methods differs between the domains of image data for training. Generative models are researched to use for augmenting data recently. In particular, Generative Adversarial Networks (GANs) (Goodfellow et al., 2014) that can generate clean image get attention as an excellent innovation in machine learning. As GANs extension method, there is a method called CGANs (Mirza and Osindero, 2014) that can be used for data augmentation. When enough training data for each class are not prepared for classification model, the same is true for training CGANs. In such case, CGAN generates noisy images. This makes a classification model to underfit to the original training data. Moreover, when a CGAN approximates the training data distribution, the CGAN generates new training data in the same region where training data densely exist. In such case, augmented data can’t reduce overfitting on the original training data. Therefore, our research contributes to augment data which meets these two requirements. In this study, we propose a method to generate data by the class specific GAN with small training data and selectively add generated data to the training data set that improves classification accuracy by using the entropy of the classification model. The feature of the proposed method is that it focuses on the positional relationship between data and the classification hyperplane in deep learning. In the proposed method, the entropy of the classification model is used to measure the positional relationship between the classification boundary and the data. As a result, the generalization performance is improved by adding the data around the classification boundary as new training data.

A Latent Class Analysis for Item Demand Based on Temperature Difference and Store Characteristics

Yuto Seko,Ryotaro Shimizu,Gendo Kumoi,Tomohiro Yoshikai,Masayuki Goto 대한산업공학회 2021 Industrial Engineeering & Management Systems Vol.20 No.1

In retail stores, there is an increasing need for predicting item demand using accumulated purchase history data to cope with the fluctuating consumer demands. These fluctuations in item demand are influenced by external factors and consumer preferences. Among these, store characteristics and weather conditions, which are closely related to consumer behavior, have strong effects on item demand. For this reason, it is very important to quantitatively grasp demand fluctuations of items that are influenced by changes in weather conditions for each store by using an integrated analysis of the purchase history data of many stores and weather conditions. In this research, we focus on the temperature difference, which is the average temperature difference from the previous day, as a weather condition affecting item sales. Because consumer feeling about a temperature is dependent on the temperature difference from the previous day, it is meaningful to construct a prediction model using this information. In this research, we propose a latent class model to express the relationship between weather conditions, store characteristics, and item demand fluctuation. Also, through an analysis experiment using an actual data set, we show the usefulness of the proposed model by extracting items that are influenced by weather conditions.

Airborne Ku-Band Antenna Subsystem for Satellite Communications

Nuimura, Shuji,Horie, Toshiyuki,Sato, Hiroyuki,Naito, Izuru,Kumoi, Kazunari,Yoshizawa, Hidenori,Konishi, Yoshihiko,Takeuchi, Norio,Shimawaki, Yutaka 통신위성우주산업연구회 2004 Joint Conference on Satellite Communications Vol.2004 No.-

An Airborne Antenna Subsystem (AAS) is presented for Ku-band airborne broadband satellite communications. The AAS employs ultra-low profile dual reflector mechanical scanned antenna to reduce the additional aerodynamic drag with high RF performance up to low elevation beam direction. The AAS achieves precise satellite tracking capability under the severe dynamic condition of airplane. In addition, the AAS achieves precise polarization tracking capability for FSS satellite transponder application. Furthermore, wide-variety of novel technologies have been developed and applied to the AAS, such as high efficiency SSPAs, ultra-thin broadband OMT, etc. The AAS gas been confirmed to achieve excellent performance, and practically operated since Spring of 2004.