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...

The evaluation of classifier performance in a cost-sensitive setting is straightforward if the operating conditions (misclassification costs and class distributions) are fixed and known. When this is not the case, evaluation requires a method of visualizing classifier performance across the full range of possible operating conditions. This talk outlines the most important requirements for cost-...

The present article proposes a fuzzy set-based classifier with a better learning and generalization capability. The proposed classifier exploits the feature-wise degree of belonging of a pattern to all classes, generalization in the fuzzification process and the combined class-wise contribution of features effectively. The classifier uses a -type membership function and product aggregation reas...

This article deals with the description of a new classifier for an automatic form class identification system. This new structural classifier is based on a tree comparisons. The high level information used by this classifier is presented in the article. A module first extracts the form content. The form content organisation is described in a hierarchical way modelled by a tree. This tree corres...

Viola and Jones [VJ] demonstrate that cascade classification methods can successfully detect objects belonging to a single class, such as faces. Detecting and identifying objects that belong to any of a set of “classes”, many class detection, is a much more challenging problem. We show that objects from each class can form a “cluster” in a “classifier space” and illustrate examples of such clus...

Lapata and Brew (2004) (hereafter LB04) obtain from untagged texts a statistical prior model that is able to generate class preferences for ambiguous Levin (1993) verbs (hereafter Levin). They also show that their informative priors, incorporated into a Naive Bayes classifier deduced from hand-tagged data, can aid in verb class disambiguation. We reanalyse LB04’s prior model and show that a sin...

A new ensemble of support vector machines (SVM) based on random subspace (RS) and feature selection is developed and applied to the problem of differential diagnosis of erythemato-squamous diseases. Each classifier has a ‘‘favourite’’ class. To find the feature subset for the classifier Di with ‘‘favourite’’ class wi, we calculate the best features to discriminate this class (wi) from all the o...

This paper presents a novel algorithm for eliminating class noise based on the analysis of the feature class attribute in text classification. The algorithm can eliminate class noise for classifier by mining the most representative class information of text features, which means that the algorithm can actively prejudge the candidate class labels to unseen documents using the class attribute lin...

Sub-class partition information within positive and negative classes is often ignored by a binary classifier, even when these detailed background information is available at hand. It is expected that this kind of additional information can help to improve the differentiating capacity of a binary classifier. In this paper, a binary classification strategy via multi-class categorization is propos...

Abstract Sub-class partition information within positive and negative classes is often ignored by a binary classifier, even when these detailed background information is available at hand. It is expected that this kind of additional information can help to improve the differentiating capacity of a binary classifier. In this paper, a binary classification strategy via multi-class categorization ...