Add to ORKGClassification is a well-studied problem in machine learning and data mining. Classifier performance was originally gauged almost exclusively using predictive accuracy. However, as work in the field progressed, more sophisticated measures of classifier utility that better represented the value of the induced knowledge were introduced. Nonetheless, most work still ignored the cost of acquiring training examples, even though this affects the overall utility of a classifier. In this paper we consider the costs of acquiring the training examples in the data mining process; we analyze the impact of the cost of training data on learning, identify the optimal training set size for a given data set, and analyze the performance of several progressiv...
In many domains, collecting sufficient labeled training data for supervised machine learning require...
We consider the problem of feature efficient prediction – a setting where features have costs, and t...
Machine learning algorithms have successfully entered industry through many real-world applications ...
For large, real-world inductive learning problems, the number of training examples often must be lim...
For large, real-world inductive learning problems, the number of training examples often must be lim...
For large, real-world inductive learning problems, the number of training examples often must be lim...
For large, real-world inductive learning problems, the number of training examples often must be lim...
For large, real-world inductive learning problems, the number of training examples often must be li...
It is an actual and challenging issue to learn cost-sensitive models from those datasets that are wi...
Thesis (Ph.D.)--Boston UniversityIn a typical discriminative learning setting, a set of labeled trai...
The main objective of this paper is to investigate the relationship between the size of training sam...
Many of today's large data sets must be reduced in size before invoking inductive algorithms, due to...
There is a significant body of research in machine learning addressing techniques for performing cla...
Abstract- The classifier built from a data set with a highly skewed class distribution generally pre...
We study a novel machine learning (ML) problem setting of sequentially allocating small subsets of t...
In many domains, collecting sufficient labeled training data for supervised machine learning require...
We consider the problem of feature efficient prediction – a setting where features have costs, and t...
Machine learning algorithms have successfully entered industry through many real-world applications ...
For large, real-world inductive learning problems, the number of training examples often must be lim...
For large, real-world inductive learning problems, the number of training examples often must be lim...
For large, real-world inductive learning problems, the number of training examples often must be lim...
For large, real-world inductive learning problems, the number of training examples often must be lim...
For large, real-world inductive learning problems, the number of training examples often must be li...
It is an actual and challenging issue to learn cost-sensitive models from those datasets that are wi...
Thesis (Ph.D.)--Boston UniversityIn a typical discriminative learning setting, a set of labeled trai...
The main objective of this paper is to investigate the relationship between the size of training sam...
Many of today's large data sets must be reduced in size before invoking inductive algorithms, due to...
There is a significant body of research in machine learning addressing techniques for performing cla...
Abstract- The classifier built from a data set with a highly skewed class distribution generally pre...
We study a novel machine learning (ML) problem setting of sequentially allocating small subsets of t...
In many domains, collecting sufficient labeled training data for supervised machine learning require...
We consider the problem of feature efficient prediction – a setting where features have costs, and t...
Machine learning algorithms have successfully entered industry through many real-world applications ...