Double Ensemble Approaches to Predicting Firms’ Credit Rating

Several rating agencies such as Standard & Poor\u27s (S&P), Moody\u27s and Fitch Ratings have evaluated firms’ credit rating. Since lots of fees are required by the agencies and sometimes the timely default risk of the firms is not reflected, it can be helpful for stakeholders if the credit ratings can be predicted before the agencies publish them. However, it is not easy to make an accurate prediction of credit rating since it covers a variety of range. Therefore, this study proposes two double ensemble approaches, 1) bagging-boosting and 2) boosting-bagging, to improve the prediction accuracy. To that end, we first conducted feature selection, using Chi-Square and Gain-Ratio attribute evaluators, with 3 classification algorithms (i.e., decision tree (DT), artificial neural network (ANN), and Naïve Bayesian (NB)) to select relevant features and a base classifier of ensemble models. And then, we integrated bagging and boosting methods by applying boosting method to bagging method (bagging-boosting), and bagging method to boosting method (boosting-bagging). Finally, we compared the prediction accuracy of our proposed model to benchmark models. The experimental results showed that our proposed models outperformed the benchmark models

Development of a computer-aided design supporting system for transformative product design

Modern product innovation is often delivered by fusing technologies from different domains. To meet the current speedy pressure of product innovation, the conceptual design is a very critical stage to develop an innovative product. This research presents a new design paradigm, Transformative Product Design (TPD), to meet the demands in the conceptual design. TPD aims to design a new product from a combination out of a base product and reference products, which have been developed. To expedite the TPD paradigm, Interaction Network is introduced to represent a product design by following a representative product design representation method in the teleological perspective. However, generating an interaction network of a product is very cumbersome and somewhat subjective. In the past decade, a few research works have been reported to identify functions of a product in a systematic way. They are not satisfactory to develop an interaction network for TPD systematically. To systematically generate an interaction network, this research adopts functional requirements in design documents as the source of the proposed method. The proposed method in this research aims to identify functions, structures and dynamic behaviors between structures from the functional descriptions in natural (English) language by adapting natural language-based object identification methods in the software engineering. Another aim of this research is to provide semantic capability to utilize the use of a natural language throughout the design transformation process, taking into account cross-disciplinary design knowledge that domain experts could struggle if the knowledge is beyond their expertise. To fulfill the demand, this research builds research methods for the design transformation, based on the proposed similarity score, i.e., Stem-POS based Similarity Score. By employing the proposed score, this research detects concept functions and generates transformative design alternatives with interaction networks. Additionally, for the design transformation, the degree of transformability is to check how much products are semantically related each other, by adapting the semantic similarity score. Finally the proposed methods have been implemented in a Computer-Aided Transformative Design (CATPD) supporting system with minor human involvement to a minimum. The proposed methods and the system are validated according to Stieglitz\u27s convergence types

Extended Collaborative Filtering Technique for Mitigating the Sparsity Problem

Many online shopping malls have implemented personalized recommendation systems to improve customer retention in the age of high competition and information overload. Sellers make use of these recommendation systems to survive high competition and buyers utilize them to find proper product information for their own needs. However, transaction data of most online shopping malls prevent us from using collaborative filtering (CF) technique to recommend products, for the following two reasons: 1) explicit rating information is rarely available in the transaction data; 2) the sparsity problem usually occurs in the data, which makes it difficult to identify reliable neighbors, resulting in less effective recommendations. Therefore, this paper first suggests a means to derive implicit rating information from the transaction data of an online shopping mall and then proposes a new user similarity function to mitigate the sparsity problem. The new user similarity function computes the user similarity of two users if they rated similar items, while the user similarity function of traditional CF technique computes it only if they rated common items. Results from several experiments using an online shopping mall dataset in Korea demonstrate that our approach significantly outperforms the traditional CF technique

The More the Worse? Mining Valuable Ideas with Sentiment Analysis for Idea Recommendation

Many firms have an interest in an open innovation community, recognizing its business value. They can collect and analyze the ideas of their customers from the community to get valuable ideas which can lead to innovation such as a new product or service. However, such a community overloaded with too many ideas from customers cannot make use of them at the right time because of the limited time and human resources to deal with them. Therefore, it would be a great help to those firms if they have a recommendation system which recommends top n ideas for innovation. MyStarbucksIdea (MSI) is such an open community, created by Starbucks. To build such an innovative idea recommendation system for Starbucks, we analyzed a dataset collected from MSI, utilizing data mining and sentiment analysis techniques. Experimental results show that our recommendation system can help firms identify prospective ideas which can be valuable enough for their innovation among a large amount of ideas, efficiently

A ROBUST LOCATION TRACKING USING UBIQUITOUS RFID WIRELESS NETWORK

A dangerous workplace like the iron production company needs a durable monitoring of workers to protect them from an critical accident. This paper concerns about a robust and accurate location tracking method using ubiquitous RFID wireless network. The sensed RSSI signals obtained from the RFID readers are very unstable in the complicated and propagation-hazard workplace like the iron production company. So, the existing particle filter can not provide a satisfactory location tracking performance. To overcome this limitation, we propose a double layered particle filter, where the lower layer classifies the block in which the tag is contained by the SVM classifier and the upper layer estimates the accurate location of tag owner by the particle filter within the classified block. This layered structure improves the location estimation and tracking performance because the evidence about the location from the lower layer makes a effective restrict on the range of possible locations of the upper layer. We implement the proposed location estimation and tracking system using the ubiquitous RFID wireless network in a noisy and complicated workplace (100m x 50m) where which 49 RFID readers and 9 gateways are located in the fixed locations and the maximally 100 workers owning active RFID tags are moving around the workplace. Many extensive experiments show that the proposed location estimation and tracking system is working well in a real-time and the position error is about 2m at maximum.X111sciescopu