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» Evaluating learning algorithms and classifiers
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KDD
1998
ACM
112views Data Mining» more  KDD 1998»
15 years 3 months ago
Evaluating Usefulness for Dynamic Classification
This paper develops the concept of usefulness in the context of supervised learning. We argue that usefulness can be used to improve the performance of classification rules (as me...
Gholamreza Nakhaeizadeh, Charles Taylor, Carsten L...
CEC
2007
IEEE
15 years 6 months ago
WAIRS: improving classification accuracy by weighting attributes in the AIRS classifier
— AIRS (Artificial Immune Recognition System) has shown itself to be a competitive classifier. It has also proved to be the most popular immune inspired classifier. However, rath...
Andrew Secker, Alex Alves Freitas
GECCO
2006
Springer
171views Optimization» more  GECCO 2006»
15 years 3 months ago
Evolving ensemble of classifiers in random subspace
Various methods for ensemble selection and classifier combination have been designed to optimize the results of ensembles of classifiers. Genetic algorithm (GA) which uses the div...
Albert Hung-Ren Ko, Robert Sabourin, Alceu de Souz...
CLEF
2011
Springer
13 years 11 months ago
Author Identification Using Semi-supervised Learning - Notebook for PAN at CLEF 2011
Author identification models fall into two major categories according to the way they handle the training texts: profile-based models produce one representation per author while in...
Ioannis Kourtis, Efstathios Stamatatos
ICALT
2010
IEEE
14 years 10 months ago
Course Ranking and Automated Suggestions through Web Mining
—This paper introduces new metrics for course evaluation. It is also proposes a ranking algorithm that classifies courses based on the previous course evaluation metrics and sugg...
Stavros Valsamidis, Ioannis Kazanidis, Sotirios Ko...