An Activation Method of Topic Dictionary to Expand Training Data for Trend Rule Discovery
This paper improves a method which predicts whether evaluation objects such as companies and products are to be attractive in near future. The attractiveness is evaluated by trend rules. The trend rules represent relationships among evaluation objects, keywords, and numerical changes related to the...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2014/871412 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832561923264086016 |
|---|---|
| author | Shigeaki Sakurai Kyoko Makino Shigeru Matsumoto |
| author_facet | Shigeaki Sakurai Kyoko Makino Shigeru Matsumoto |
| author_sort | Shigeaki Sakurai |
| collection | DOAJ |
| description | This paper improves a method which predicts whether evaluation objects such as companies and products are to be attractive in near future. The attractiveness is evaluated by trend rules. The trend rules represent relationships among evaluation objects, keywords, and numerical changes related to the evaluation objects. They are inductively acquired from text sequential data and numerical sequential data. The method assigns evaluation objects to the text sequential data by activating a topic dictionary. The dictionary describes keywords representing the numerical change. It can expand the amount of the training data. It is anticipated that the expansion leads to the acquisition of more valid trend rules. This paper applies the method to a task which predicts attractive stock brands based on both news headlines and stock price sequences. It shows that the method can improve the detection performance of evaluation objects through numerical experiments. |
| format | Article |
| id | doaj-art-7f38a31a8bb04408ba4aa02f19280d90 |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-7f38a31a8bb04408ba4aa02f19280d902025-02-03T01:23:55ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322014-01-01201410.1155/2014/871412871412An Activation Method of Topic Dictionary to Expand Training Data for Trend Rule DiscoveryShigeaki Sakurai0Kyoko Makino1Shigeru Matsumoto2IT Research and Development Center, Toshiba Solutions Corporation, 3-22 Katamachi, Fuchu, Tokyo 183-8512, JapanIT Research and Development Center, Toshiba Solutions Corporation, 3-22 Katamachi, Fuchu, Tokyo 183-8512, JapanIT Research and Development Center, Toshiba Solutions Corporation, 3-22 Katamachi, Fuchu, Tokyo 183-8512, JapanThis paper improves a method which predicts whether evaluation objects such as companies and products are to be attractive in near future. The attractiveness is evaluated by trend rules. The trend rules represent relationships among evaluation objects, keywords, and numerical changes related to the evaluation objects. They are inductively acquired from text sequential data and numerical sequential data. The method assigns evaluation objects to the text sequential data by activating a topic dictionary. The dictionary describes keywords representing the numerical change. It can expand the amount of the training data. It is anticipated that the expansion leads to the acquisition of more valid trend rules. This paper applies the method to a task which predicts attractive stock brands based on both news headlines and stock price sequences. It shows that the method can improve the detection performance of evaluation objects through numerical experiments.http://dx.doi.org/10.1155/2014/871412 |
| spellingShingle | Shigeaki Sakurai Kyoko Makino Shigeru Matsumoto An Activation Method of Topic Dictionary to Expand Training Data for Trend Rule Discovery Applied Computational Intelligence and Soft Computing |
| title | An Activation Method of Topic Dictionary to Expand Training Data for Trend Rule Discovery |
| title_full | An Activation Method of Topic Dictionary to Expand Training Data for Trend Rule Discovery |
| title_fullStr | An Activation Method of Topic Dictionary to Expand Training Data for Trend Rule Discovery |
| title_full_unstemmed | An Activation Method of Topic Dictionary to Expand Training Data for Trend Rule Discovery |
| title_short | An Activation Method of Topic Dictionary to Expand Training Data for Trend Rule Discovery |
| title_sort | activation method of topic dictionary to expand training data for trend rule discovery |
| url | http://dx.doi.org/10.1155/2014/871412 |
| work_keys_str_mv | AT shigeakisakurai anactivationmethodoftopicdictionarytoexpandtrainingdatafortrendrulediscovery AT kyokomakino anactivationmethodoftopicdictionarytoexpandtrainingdatafortrendrulediscovery AT shigerumatsumoto anactivationmethodoftopicdictionarytoexpandtrainingdatafortrendrulediscovery AT shigeakisakurai activationmethodoftopicdictionarytoexpandtrainingdatafortrendrulediscovery AT kyokomakino activationmethodoftopicdictionarytoexpandtrainingdatafortrendrulediscovery AT shigerumatsumoto activationmethodoftopicdictionarytoexpandtrainingdatafortrendrulediscovery |