Please use this identifier to cite or link to this item:
https://olympias.lib.uoi.gr/jspui/handle/123456789/11079Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Tripoliti, E. | en |
| dc.contributor.author | Fotiadis, D. | en |
| dc.contributor.author | Manis, G. | en |
| dc.date.accessioned | 2015-11-24T17:02:38Z | - |
| dc.date.available | 2015-11-24T17:02:38Z | - |
| dc.identifier.issn | 1089-7771 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/11079 | - |
| dc.rights | Default Licence | - |
| dc.title | Automated diagnosis of diseases based on classification: dynamic determination of the number of trees in Random Forests algorithm | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | 10.1109/titb.2011.2175938 | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.publicationDate | 2011 | - |
| heal.abstract | The accurate diagnosis of diseases with high prevalence rate, such as Alzheimer, Parkinson, Diabetes, Breast Cancer and heart diseases, is one of the most important biomedical problems whose administration is imperative. In this work we present a new method for the automated diagnosis of diseases based on the improvement of Random Forests classification algorithm. More specifically, the dynamic determination of the optimum number of base classifiers composing the Random Forests is addressed. The proposed method is different from most of the methods reported in the literature which follow an overproduce-and-choose strategy, where the members of the ensemble are selected from a pool of classifiers which is known a priori. In our case the number of classifiers is determined during the growing procedure of the forest. Additionally, the proposed method produces an ensemble not only accurate but also diverse, ensuring the two important properties that should characterize an ensemble classifier. The method is based on an online fitting procedure and it is evaluated using eight biomedical datasets and five versions of the Random Forests algorithm (40 cases). The method decided correctly the number of trees in 90% of the test cases. | en |
| heal.journalName | Information Technology in Biomedicine, IEEE Transactions on | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Manis-2012-Automated diagnosis of diseases based on.pdf | 460.42 kB | Adobe PDF | View/Open Request a copy |
This item is licensed under a Creative Commons License
