Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal International Journal of Computer and Information System (IJCIS) Malcom: Indonesian Journal of Machine Learning and Computer Science
Duan, Lianzhai
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Machine Learning Modeling for Forecasting Repeat Purchases in Online Shopping Duan, Lianzhai
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 3 (2024): MALCOM July 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i3.1388

Abstract

Online shopping merchants will conduct a series of marketing activities to increase customers, but in many cases, most of the new customers will not make repeat purchases, which is not conducive to the long-term interests of the merchants. Therefore, it is important for merchants to target users who are more likely to repurchase, as this can reduce marketing costs and increase ROI. Based on the dataset provided by the online shopping website, this paper conducts mining and exploratory analysis of the data, utilizes feature engineering methodology, and modeling analysis using LightGBM, Logistic, Xgboost for machine learning modeling. Meanwhile, parameter optimization and model evaluation verification are performed, Finally, the comparative analysis resulted in LightGBM as the best prediction model, will provide efficient marketing decisions for the operation of online shopping stores.
Research on Relation Extraction Method Based on Active Learning Duan, Lianzhai
International Journal of Computer and Information System (IJCIS) Vol 5, No 2 (2024): IJCIS : Vol 5 - Issue 2 - 2024
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v5i2.166

Abstract

The knowledge in contemporary society has exploded, and the most common knowledge is contained in unstructured natural language texts. Information Extraction technology expresses semantic knowledge in unstructured text through a set of mentioned entities, the relationships between these entities, and the events in which these entities participate. As a key part of information extraction, Relation Extraction technology provides important theoretical basis and use value for text knowledge understanding by judging the relationships between given entities. Currently, relationship extraction based on supervised learning requires a large number of labeled samples. Randomly selecting some data labels is not only a waste of data resources, but also directly affects the final accuracy of the classification model. In fact, with the development of data collection and storage technology, it has become very easy to obtain a large amount of unlabeled natural language text. Therefore, it is of great practical value to design an algorithm that can effectively utilize unlabeled sample sets for relationship extraction. In order to solve the above problems, this paper uses active learning as the starting point to implement a variety of sampling algorithms, mainly including uncertainty, diversity, representativeness and other algorithms. On the basis of verifying that active learning is suitable for relationship extraction tasks, through the fusion of multiple This sampling criterion ultimately yields an active learning sample selection strategy that is still effective under multiple data sets and multiple learning models. Experiments have proven that the multi-criteria fusion sampling strategy proposed in this article is an effective and universal strategy. Compared with multiple single-strategy sampling algorithms, it can achieve equivalent or higher classification accuracy on multiple data sets.
Co-Authors