Add to ORKGAbstract: There are many methods to design classifiers for the supervised classification problem. In this paper, we study the problem of aggregating classifiers. We construct an algorithm to count the number of distinct aggre-gate classifiers. This leads to a new way of finding a best aggregate classifier. When there are only two classes, we explore the link between aggregating classifiers and n-bit boolean functions. Further, the sequence of the number of distinct aggregated classifiers appears to be new
This paper introduces a novel concept for creating an ensemble of classifiers. The concept is based ...
We consider the following problem: given a set of clus-terings, find a clustering that agrees as muc...
This article develops an efficient combinatorial algorithm based on labeled directed graphs and moti...
There are many methods to design classifiers for the supervised classification problem. In this pape...
AbstractWe propose a generic model for the “weighted voting” aggregation step performed by several m...
We propose a generic model for the "weighted voting" aggregation step performed by several methods ...
Traditionally, bagging takes a majority vote among a number of classifiers. An alternative is to agg...
In recent years the introduction of aggregation methods led to many new techniques within the field ...
Suppose that a group of individuals must classify objects into three or more categories, and does so...
Usage of recognition systems has found many applications in almost all fields. However, Most of clas...
Over the last two decades, the machine learning and related communities have conducted numerous stud...
© 2016 Elsevier Inc. This paper introduces a novel algorithm, called Supervised Aggregated FEature l...
Two class classification problems in real world are often characterized by imbalanced classes. This ...
AbstractA team of learning machines is a multiset of learning machines. A team is said to be success...
Combining the outputs of multiple neural networks has led to substantial improvements in several dif...
This paper introduces a novel concept for creating an ensemble of classifiers. The concept is based ...
We consider the following problem: given a set of clus-terings, find a clustering that agrees as muc...
This article develops an efficient combinatorial algorithm based on labeled directed graphs and moti...
There are many methods to design classifiers for the supervised classification problem. In this pape...
AbstractWe propose a generic model for the “weighted voting” aggregation step performed by several m...
We propose a generic model for the "weighted voting" aggregation step performed by several methods ...
Traditionally, bagging takes a majority vote among a number of classifiers. An alternative is to agg...
In recent years the introduction of aggregation methods led to many new techniques within the field ...
Suppose that a group of individuals must classify objects into three or more categories, and does so...
Usage of recognition systems has found many applications in almost all fields. However, Most of clas...
Over the last two decades, the machine learning and related communities have conducted numerous stud...
© 2016 Elsevier Inc. This paper introduces a novel algorithm, called Supervised Aggregated FEature l...
Two class classification problems in real world are often characterized by imbalanced classes. This ...
AbstractA team of learning machines is a multiset of learning machines. A team is said to be success...
Combining the outputs of multiple neural networks has led to substantial improvements in several dif...
This paper introduces a novel concept for creating an ensemble of classifiers. The concept is based ...
We consider the following problem: given a set of clus-terings, find a clustering that agrees as muc...
This article develops an efficient combinatorial algorithm based on labeled directed graphs and moti...