| Title | New results on minimum error entropy decision trees |
| Publication Type | Book |
| Year of Publication | 2011 |
| Authors | De Sá, JPM, Sebastião, R, Gama, J, Fontes, T |
| Series Title | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci. |
| Volume | 7042 LNCS |
| Number of Pages | 355 - 362 |
| City | Pucon |
| ISBN Number | 03029743 (ISSN); 9783642250842 (ISBN) |
| Keywords | Algorithms, Alternative algorithms, Computer vision, Decision Theory, decision trees, entropy, entropy-of-error, Error entropy, Error performance, Forestry, New results, node split criteria, Real-world datasets, Vision |
| Abstract | We present new results on the performance of Minimum Error Entropy (MEE) decision trees, which use a novel node split criterion. The results were obtained in a comparive study with popular alternative algorithms, on 42 real world datasets. Carefull validation and statistical methods were used. The evidence gathered from this body of results show that the error performance of MEE trees compares well with alternative algorithms. An important aspect to emphasize is that MEE trees generalize better on average without sacrifing error performance. © 2011 Springer-Verlag. |
| URL | http://www.scopus.com/inward/record.url?eid=2-s2.0-81855161442&partnerID=40&md5=0d71ee4459a1a6a01536cfec0ff05664 |
