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| Title | Overfitting or Poor Learning : A Critique of Current Financial Applications of GP |
| Creator | 陳樹衡 Chen,Shu-Heng |
| Contributor | 經濟系 |
| Date | 2003 |
| Date Issued | 14-Aug-2014 11:43:23 (UTC+8) |
| Summary | Motivated by a measure of predictability, this paper uses the extracted signal ratio as a measure of the degree of overfitting. With this measure, we examine the performance of one type of overfittingavoidance design frequently used in financial applications of GP. Based on the simulation results run with the software Simple GP, we find that this design is not effective in avoiding overfitting. Furthermore, within the range of search intensity typically considered by these applications, we find that underfitting, instead of overfitting, is the more prevalent problem. This problem becomes more serious when the data is generated by a process that has a high degree of algorithmic complexity. This paper, therefore, casts doubt on the conclusions made by those early applications regarding the poor performance of GP, and recommends that changes be made to ensure progress. |
| Relation | Genetic Programming Lecture Notes in Computer Science Volume 2610, 2003, pp 34-46 |
| Type | book/chapter |
| DOI | http://dx.doi.org/10.1007/3-540-36599-0_4 |
| dc.contributor | 經濟系 | en_US |
| dc.creator (作者) | 陳樹衡 | zh_TW |
| dc.creator (作者) | Chen,Shu-Heng | en_US |
| dc.date (日期) | 2003 | en_US |
| dc.date.accessioned | 14-Aug-2014 11:43:23 (UTC+8) | - |
| dc.date.available | 14-Aug-2014 11:43:23 (UTC+8) | - |
| dc.date.issued (上傳時間) | 14-Aug-2014 11:43:23 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/68715 | - |
| dc.description.abstract (摘要) | Motivated by a measure of predictability, this paper uses the extracted signal ratio as a measure of the degree of overfitting. With this measure, we examine the performance of one type of overfittingavoidance design frequently used in financial applications of GP. Based on the simulation results run with the software Simple GP, we find that this design is not effective in avoiding overfitting. Furthermore, within the range of search intensity typically considered by these applications, we find that underfitting, instead of overfitting, is the more prevalent problem. This problem becomes more serious when the data is generated by a process that has a high degree of algorithmic complexity. This paper, therefore, casts doubt on the conclusions made by those early applications regarding the poor performance of GP, and recommends that changes be made to ensure progress. | en_US |
| dc.format.extent | 305027 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.language.iso | en_US | - |
| dc.relation (關聯) | Genetic Programming Lecture Notes in Computer Science Volume 2610, 2003, pp 34-46 | en_US |
| dc.title (題名) | Overfitting or Poor Learning : A Critique of Current Financial Applications of GP | en_US |
| dc.type (資料類型) | book/chapter | en |
| dc.identifier.doi (DOI) | 10.1007/3-540-36599-0_4 | - |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1007/3-540-36599-0_4 | - |