• Title/Summary/Keyword: Business Classification Systems

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An Application of Support Vector Machines to Customer Loyalty Classification of Korean Retailing Company Using R Language

  • Nguyen, Phu-Thien;Lee, Young-Chan
    • The Journal of Information Systems
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    • v.26 no.4
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    • pp.17-37
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    • 2017
  • Purpose Customer Loyalty is the most important factor of customer relationship management (CRM). Especially in retailing industry, where customers have many options of where to spend their money. Classifying loyal customers through customers' data can help retailing companies build more efficient marketing strategies and gain competitive advantages. This study aims to construct classification models of distinguishing the loyal customers within a Korean retailing company using data mining techniques with R language. Design/methodology/approach In order to classify retailing customers, we used combination of support vector machines (SVMs) and other classification algorithms of machine learning (ML) with the support of recursive feature elimination (RFE). In particular, we first clean the dataset to remove outlier and impute the missing value. Then we used a RFE framework for electing most significant predictors. Finally, we construct models with classification algorithms, tune the best parameters and compare the performances among them. Findings The results reveal that ML classification techniques can work well with CRM data in Korean retailing industry. Moreover, customer loyalty is impacted by not only unique factor such as net promoter score but also other purchase habits such as expensive goods preferring or multi-branch visiting and so on. We also prove that with retailing customer's dataset the model constructed by SVMs algorithm has given better performance than others. We expect that the models in this study can be used by other retailing companies to classify their customers, then they can focus on giving services to these potential vip group. We also hope that the results of this ML algorithm using R language could be useful to other researchers for selecting appropriate ML algorithms.

Multi-Label Classification for Corporate Review Text: A Local Grammar Approach (머신러닝 기반의 기업 리뷰 다중 분류: 부분 문법 적용을 중심으로)

  • HyeYeon Baek;Young Kyun Chang
    • Information Systems Review
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    • v.25 no.3
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    • pp.27-41
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    • 2023
  • Unlike the previous works focusing on the state-of-the-art methodologies to improve the performance of machine learning models, this study improves the 'quality' of training data used in machine learning. We propose a method to enhance the quality of training data through the processing of 'local grammar,' frequently used in corpus analysis. We collected a vast amount of unstructured corporate review text data posted by employees working in the top 100 companies in Korea. After improving the data quality using the local grammar process, we confirmed that the classification model with local grammar outperformed the model without it in terms of classification performance. We defined five factors of work engagement as classification categories, and analyzed how the pattern of reviews changed before and after the COVID-19 pandemic. Through this study, we provide evidence that shows the value of the local grammar-based automatic identification and classification of employee experiences, and offer some clues for significant organizational cultural phenomena.

Applications of Machine Learning Models on Yelp Data

  • Ruchi Singh;Jongwook Woo
    • Asia pacific journal of information systems
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    • v.29 no.1
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    • pp.35-49
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    • 2019
  • The paper attempts to document the application of relevant Machine Learning (ML) models on Yelp (a crowd-sourced local business review and social networking site) dataset to analyze, predict and recommend business. Strategically using two cloud platforms to minimize the effort and time required for this project. Seven machine learning algorithms in Azure ML of which four algorithms are implemented in Databricks Spark ML. The analyzed Yelp business dataset contained 70 business attributes for more than 350,000 registered business. Additionally, review tips and likes from 500,000 users have been processed for the project. A Recommendation Model is built to provide Yelp users with recommendations for business categories based on their previous business ratings, as well as the business ratings of other users. Classification Model is implemented to predict the popularity of the business as defining the popular business to have stars greater than 3 and unpopular business to have stars less than 3. Text Analysis model is developed by comparing two algorithms, uni-gram feature extraction and n-feature extraction in Azure ML studio and logistic regression model in Spark. Comparative conclusions have been made related to efficiency of Spark ML and Azure ML for these models.

A Study on the Usage of STEP data on the Construction CALS/EC Environment - Focusing on linking the Drawing Information and Material Information - (건설 CALS/EC 환경에서의 STEP 데이터 활용방안에 관한 연구 - 도면정보와 자재정보 연계 중심으로 -)

  • 서종철;김인한
    • The Journal of Society for e-Business Studies
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    • v.8 no.1
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    • pp.121-139
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    • 2003
  • Currently, it is not popular to use the STEP based product information in the construction drawing files, in spite of the importance and possibility of using various product data in drawing files on the CALS/EC environment. This paper aims to demonstrate a construction drawing information management system based on ISO 10303/STEP. To achieve this aim, the authors have analyzed the current construction drawing information classification hierarchy widely used for domestic and international, and examined the material data connection mechanism within CAD drawing data, and finally investigated the management systems for construction documentations and drawings in a public companies. Therefore, the expected benefit of the proposed system is that STEP drawing information management will be done standardization and the information of STEP construction drawing can be managed, shared and supported design business through materials data connection.

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Research on Chinese Microblog Sentiment Classification Based on TextCNN-BiLSTM Model

  • Haiqin Tang;Ruirui Zhang
    • Journal of Information Processing Systems
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    • v.19 no.6
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    • pp.842-857
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    • 2023
  • Currently, most sentiment classification models on microblogging platforms analyze sentence parts of speech and emoticons without comprehending users' emotional inclinations and grasping moral nuances. This study proposes a hybrid sentiment analysis model. Given the distinct nature of microblog comments, the model employs a combined stop-word list and word2vec for word vectorization. To mitigate local information loss, the TextCNN model, devoid of pooling layers, is employed for local feature extraction, while BiLSTM is utilized for contextual feature extraction in deep learning. Subsequently, microblog comment sentiments are categorized using a classification layer. Given the binary classification task at the output layer and the numerous hidden layers within BiLSTM, the Tanh activation function is adopted in this model. Experimental findings demonstrate that the enhanced TextCNN-BiLSTM model attains a precision of 94.75%. This represents a 1.21%, 1.25%, and 1.25% enhancement in precision, recall, and F1 values, respectively, in comparison to the individual deep learning models TextCNN. Furthermore, it outperforms BiLSTM by 0.78%, 0.9%, and 0.9% in precision, recall, and F1 values.

Robust Real-time Intrusion Detection System

  • Kim, Byung-Joo;Kim, Il-Kon
    • Journal of Information Processing Systems
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    • v.1 no.1 s.1
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    • pp.9-13
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    • 2005
  • Computer security has become a critical issue with the rapid development of business and other transaction systems over the Internet. The application of artificial intelligence, machine learning and data mining techniques to intrusion detection systems has been increasing recently. But most research is focused on improving the classification performance of a classifier. Selecting important features from input data leads to simplification of the problem, and faster and more accurate detection rates. Thus selecting important features is an important issue in intrusion detection. Another issue in intrusion detection is that most of the intrusion detection systems are performed by off-line and it is not a suitable method for a real-time intrusion detection system. In this paper, we develop the real-time intrusion detection system, which combines an on-line feature extraction method with the Least Squares Support Vector Machine classifier. Applying the proposed system to KDD CUP 99 data, experimental results show that it has a remarkable feature extraction and classification performance compared to existing off-line intrusion detection systems.

Analysis of the Domestic Construction Industry Classification System through an Overseas Construction Industry Case Study (해외 건설산업의 사례에 의한 국내 건설 업종 분류체계의 비교 분석)

  • Kim, Jeong-wook;Kim, Gyu-yong;Choi, Min-soo;Nam, Jeong-soo;Lee, Sang-soo
    • Journal of the Korea Institute of Building Construction
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    • v.22 no.5
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    • pp.463-471
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    • 2022
  • The construction industry has a structure in which information asymmetry problems are complexly scattered compared to other industries. Since the construction industry classification system serves as a guideline for entering the construction market and can provide as a standard for construction consumers to select a supplier who can provide appropriate services, when judging the operation purpose or purpose of the construction industry registration system, it is very important to set up the system by rationally reviewing it. The purpose of this study is to examine the possibility of improvement in consideration of the risk factors related to the domestic construction industry registration industry classification system. To this end, we will conduct a case study on the construction industry classification system operated by overseas construction industry licenses or registration systems in Japan, the United States, and Australia, and compare it with the domestic industry classification system to derive implications and directions for improvement.

A Study on the Service Integration of Traditional Chatbot and ChatGPT (전통적인 챗봇과 ChatGPT 연계 서비스 방안 연구)

  • Cheonsu Jeong
    • Journal of Information Technology Applications and Management
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    • v.30 no.4
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    • pp.11-28
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    • 2023
  • This paper proposes a method of integrating ChatGPT with traditional chatbot systems to enhance conversational artificial intelligence(AI) and create more efficient conversational systems. Traditional chatbot systems are primarily based on classification models and are limited to intent classification and simple response generation. In contrast, ChatGPT is a state-of-the-art AI technology for natural language generation, which can generate more natural and fluent conversations. In this paper, we analyze the business service areas that can be integrated with ChatGPT and traditional chatbots, and present methods for conducting conversational scenarios through case studies of service types. Additionally, we suggest ways to integrate ChatGPT with traditional chatbot systems for intent recognition, conversation flow control, and response generation. We provide a practical implementation example of how to integrate ChatGPT with traditional chatbots, making it easier to understand and build integration methods and actively utilize ChatGPT with existing chatbots.

Feature Selection for Multi-Class Support Vector Machines Using an Impurity Measure of Classification Trees: An Application to the Credit Rating of S&P 500 Companies

  • Hong, Tae-Ho;Park, Ji-Young
    • Asia pacific journal of information systems
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    • v.21 no.2
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    • pp.43-58
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    • 2011
  • Support vector machines (SVMs), a machine learning technique, has been applied to not only binary classification problems such as bankruptcy prediction but also multi-class problems such as corporate credit ratings. However, in general, the performance of SVMs can be easily worse than the best alternative model to SVMs according to the selection of predictors, even though SVMs has the distinguishing feature of successfully classifying and predicting in a lot of dichotomous or multi-class problems. For overcoming the weakness of SVMs, this study has proposed an approach for selecting features for multi-class SVMs that utilize the impurity measures of classification trees. For the selection of the input features, we employed the C4.5 and CART algorithms, including the stepwise method of discriminant analysis, which is a well-known method for selecting features. We have built a multi-class SVMs model for credit rating using the above method and presented experimental results with data regarding S&P 500 companies.

Automatic Classification of Power System Harmonic Disturbances (전력시스템 고조파 외란의 자동식별)

  • Kim, Byoung-Chul;Kim, Hyun-Soo;Nam, Sang-Won
    • Journal of Institute of Control, Robotics and Systems
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    • v.6 no.7
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    • pp.551-558
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    • 2000
  • In this paper a systematic approach to automatic classificationi of power system harmonic disturbances is proposed where the proposed approach consists of the following three steps:(i) detecting and localizing each harmonic disturbance by applying discrete wavelet transform(DWT) (ii) extracting an efficient feature vector from each detected disturbance waveform by utilizing FFT and principal component analysis (PCA) along with Fisher's criterion and (iii) classifying the corresponding type of each harmonic disturbance by recognizing the pattern of each feature vector. To demonstrate the performance and applicability of the proposed classification procedure some simulation results obtained by analyzing 8-class power system harmonic disturbances being generated with Matlab power system blockset are also provided.

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