Accuracy–diversity based pruning of classifier ensembles
نویسندگان
چکیده
منابع مشابه
Effective Pruning of Neural Network Classifier Ensembles
Neural network ensemble techniques have been shown to be very accurate classification techniques. However, in some real-life applications a number of classifiers required to achieve a reasonable accuracy is enormously large and hence very space consuming. This paper proposes several methods for pruning neural network ensembles. The clustering based approach applies k-means clustering to entire ...
متن کاملPruning GP-Based Classifier Ensembles by Bayesian Networks
Classifier ensemble techniques are effectively used to combine the responses provided by a set of classifiers. Classifier ensembles improve the performance of single classifier systems, even if a large number of classifiers is often required. This implies large memory requirements and slow speeds of classification, making their use critical in some applications. This problem can be reduced by s...
متن کاملCollective-agreement-based pruning of ensembles
The main idea of ensemble methodology is to weigh several individual pattern classifiers, and combine them to reach a better classification performance. Nevertheless, some ensembles superfluously contain too many members, which results in large storage requirements and in some cases it may even reduce classification performance. The goal of ensemble pruning is to identify a subset of ensemble m...
متن کاملPrototype Based Classifier Design with Pruning
An algorithm is proposed to prune the prototype vectors (prototype selection) used in a nearest neighbor classifier so that a compact classifier can be obtained with similar or even better performance. The pruning procedure is error based; a prototype will be pruned if its deletion leads to the smallest classification error increase. Also each pruning iteration is followed by one epoch of Learn...
متن کاملPruning variable selection ensembles
In the context of variable selection, ensemble learning has gained increasing interest due to its great potential to improve selection accuracy and to reduce false discovery rate. A novel ordering-based selective ensemble learning strategy is designed in this paper to obtain smaller but more accurate ensembles. In particular, a greedy sorting strategy is proposed to rearrange the order by which...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Progress in Artificial Intelligence
سال: 2014
ISSN: 2192-6352,2192-6360
DOI: 10.1007/s13748-014-0042-9