A Meta-analysis on Classification Model Performance in Real-World Datasets: An Exploratory View
| Autor: | David Gómez Guillén, Alfonso Rojas Espinosa |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Departament d'Enginyeria Telemàtica, Universitat Politècnica de Catalunya. GRXCA - Grup de Recerca en Xarxes de Comunicacions Cel·lulars i Ad-hoc |
| Rok vydání: | 2017 |
| Předmět: |
Artificial intelligence
Optimization algorithm business.industry Computer science Intel·ligència artificial 02 engineering and technology Machine learning computer.software_genre 01 natural sciences Set (abstract data type) 010104 statistics & probability Exploratory data analysis Ranking Artificial Intelligence Meta-analysis 0202 electrical engineering electronic engineering information engineering No free lunch in search and optimization Informàtica::Intel·ligència artificial [Àrees temàtiques de la UPC] 020201 artificial intelligence & image processing 0101 mathematics business computer |
| Zdroj: | Recercat. Dipósit de la Recerca de Catalunya instname UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| ISSN: | 1087-6545 0883-9514 |
| DOI: | 10.1080/08839514.2018.1430993 |
| Popis: | The No Free Lunch (NFL) Theorem imposes a theoretical restriction on optimization algorithms and their equal average performance on different problems, under some particular assumptions. Nevertheless, when brought into practice, a perceived “ranking” on the performance is usually perceived by engineers developing machine learning applications. Questions that naturally arise are what kinds of biases the real world has and in which ways can we take advantage from them. Using exploratory data analysis (EDA) on classification examples, we gather insight on some traits that set apart algorithms, datasets and evaluation measures and to what extent the NFL theorem, a theoretical result, applies under typical real-world constraints. |
| Databáze: | OpenAIRE |
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