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A Selective Multiple Instance Transfer Learning Method for Text Categorization Problems
- Source :
- Knowledge-Based Systems. 141:178-187
- Publication Year :
- 2018
- Publisher :
- Elsevier BV, 2018.
-
Abstract
- Multiple instance learning (MIL) is a generalization of supervised learning which attempts to learn a distinctive classifier from bags of instances. This paper addresses the problem of the transfer learning-based multiple instance method for text categorization problem. To provide a safe transfer of knowledge from a source task to a target task, this paper proposes a new approach, called selective multiple instance transfer learning (SMITL), which selects the case that the multiple instance transfer learning will work in step one, and then builds a multiple instance transfer learning classifier in step two. Specifically, in the first step, we measure whether the source task and the target task are related or not by investigating the similarity of the positive features of both tasks. In the second step, we construct a transfer learning-based multiple instance method to transfer knowledge from a source task to a target task if both tasks are found to be related in the first step. Our proposed approach explicitly addresses the problem of safe transfer of knowledge for multiple instance learning on the text classification problem. Extensive experiments have shown that SMITL can determine whether the two tasks are related for most data sets, and outperforms classic multiple instance learning methods.
- Subjects :
- Information Systems and Management
business.industry
Computer science
Supervised learning
02 engineering and technology
Machine learning
computer.software_genre
Management Information Systems
Text categorization
Categorization
Artificial Intelligence
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Instance-based learning
Artificial intelligence
business
Transfer of learning
Classifier (UML)
computer
Software
Subjects
Details
- ISSN :
- 09507051
- Volume :
- 141
- Database :
- OpenAIRE
- Journal :
- Knowledge-Based Systems
- Accession number :
- edsair.doi...........a4bf0b41440521f9e13734c11d3f9b24