Abstract The H-measure is a classifier performance measure which takes into account the context of application without requiring rigid value relative misclassification costs to be set. Since its introduction in 2009 it has become widely adopted. This paper answers various queries users have raised since introduction, including questions about interpretation, choice weighting function, whether s...

in this paper, the performance of 11 different distances for image retrieval and classification, based on color, shape and texture, is evaluated. the precision-recall measure and the correct classification rate of the k-nn classifier are used to evaluate retrieval and classification performances, respectively. the experimental results for a database of 1000 images from 10 different semantic gro...

The extended classifier systems (XCS) by producing a set of rules is (classifier) trying to solve learning problems as online. XCS is a rather complex combination of genetic algorithm and reinforcement learning that using genetic algorithm tries to discover the encouraging rules and value them by reinforcement learning. Among the important factors in the performance of XCS is the possibility to...

Between the various biometric methods, Face Recognition has become one of the most burning topic tasks in the pattern recognition field during the past decades. In This Work a Face Recognition System has been developed By applying different multiple classifier selection schemes on the output of three different classification methods namely Artificial Neural Network, Genetic Algorithm And Euclid...

This paper introduces a new cost function for evaluating the multi-class classifier. The new cost function facilitates both a way to visualize the performance (expected cost) of the multi-class classifier and a summary of the misclassification costs. This function overcomes the limitations of ROC in not being able to represent the classifier performance graphically when there are more than two ...

The purpose of this article is to present a new procedure for industrial process diagnosis. This method is based on bayesian classifiers. A feature selection is done before the classification between the different faults of a process. The feature selection is based on a new result about mutual information that we demonstrate. The performances of this method are evaluated on the data of a benchm...

In this new age of digital multimedia content and internet audiences, the entertainment industry and the path to stardom is slowly evolving. Many aspiring musicians can now achieve fame by performing the works of other famous artists, and sharing their performances on sites such as Youtube. Aspiring artists and other content generators on Youtube are always advised by marketing experts to inclu...

Previous research shows that keyphrases are useful tools in document retrieval and navigation. While these point to a relation between keyphrases and document retrieval performance, no other work uses this relationship to identify keyphrases of a given document. This work aims to establish a link between the problems of Query Performance Prediction (QPP) and keyphrase extraction. To this end, f...

Data classification plays important role in the field of data mining. The increasing rate of data diversity and size decrease the performance and efficiency of classifier. The decreasing performance of classifier compromised with unvoted data of classifier. Now the merging of two or more classifier for better prediction and voting of data are used, such techniques are called Ensemble classifier...

We study a distributed training of a linear classifier in which the data is separated into many shards and each worker only has access to its own shard. The goal of this distributed training is to utilize the data of all shards to obtain a wellperforming linear classifier. The iterative parameter mixture (IPM) framework (Mann et al., 2009) is a state-of-the-art distributed learning framework th...