A New One Class Classifier Based on Ensemble of Binary Classifiers.

Error correcting output code (ECOC) is a general framework of solving a multiclass classification problem via a binary class classifier ensemble. ... The approach can construct multiple different strong binary class classifiers on a given binary-class problem, so that the heuristic training process will not be stopped by some difficult binary-class ... The layered structure, as examined in [9] , Williams proves the discriminability of a classifier ensemble on a binary-class problem. ...

doi:10.5120/ijca2015906889 fatcat:fxsqw6plq5cjzeyuidf7gevshu

In this work, we focus on machine learning aspect of steganalyzer design and utilize a hierarchical ensemble of classifiers based approach to tackle two main issues. ... Secondly, since the approach can be readily extended to multi-class classification it can also be used to infer the steganographic technique deployed in generation of a stego-object. ... When a new batch of training data is available, the composite steganalyzer is updated by adding new base-classifiers to the ensemble. ...

doi:10.1109/icpr.2010.1064 dblp:conf/icpr/BayramDSM10 fatcat:kvggbfe7szb7lniwwpybaqicae

The proposed model is an Artificial Neural Network ensemble in which the base learners are composed by the union of a binary classifier and a multiclass classifier. ... In this paper, we propose a new learning model applicable to multi-class domains in which the examples are described by a large number of features. ... The biggest difference compared to the one against all architecture is that, in this new model, the ensemble members will not be binary classifiers, but modules composed of a binary classifier and a multi-class ...

doi:10.1007/s00521-010-0458-5 fatcat:ifdwzzfocjdoxex4z6wivaap74

We proposed a new hybrid ensemble for intrusion detection based on Error Correcting Output Code (ECOC) approach. ... We test the performance of seven classifiers using Bagging and AdaBoost ensemble methods. ... This ensemble based on Error Correcting Output Code (ECOC)approach, which is one of the multiclass binary classification methods. II. ...

doi:10.1109/his.2014.7086194 dblp:conf/his/AbdElrahmanA14 fatcat:eqkxek5qengxtabnenjtrzabwa

This study combined three ensemble models and proposed a new model termed the "hybrid model". ... Performance comparison of the classifiers is provided in terms of their accuracy, area under the curve (AUC), and F1 score. ... LDA creates its own new components based on the labels of the dataset. Suppose a dataset has 'x' features then LDA creates its own new 'x-1' features. ...

doi:10.17762/turcomat.v12i6.4811 fatcat:oyv4tvrm6zcaxebeuxu5gsdfmi

DOAJ Szczepanski

The aim of a pairwise classifier is to separate one class from another one. ... Indeed although usage of pairwise classification concept instead of multiclass classification concept is not new, we propose a new pairwise classifier ensemble with a very lower order. ... So first an arbitrary number of binary classifier ensembles are added to main classifier. Then results of all these binary classifier ensembles are given to a set of a heuristic based ensemble. ...

doi:10.12785/ijlms/010204 fatcat:2mqcprhnrrhhnnjlyez7brv3cu

class), then to employ the C4.5 algorithm or the KNN algorithm to create an ensemble of classifiers using each group of feature subsets resulting from a specific one of the multi-task feature selection ... training of classifiers on the selected features. ... Acknowledgements This work is supported by the Social Data Science Lab and the School of Computer Science and Informatics at the Cardiff University in the UK. ...

doi:10.1109/icmlc48188.2019.8949292 dblp:conf/icmlc/0002C19 fatcat:vqnw5y4rqfg7zh2olk65ru2h7e

for increasing the diversity of base classifiers. ... the dynamic selection of base classifiers in classification. ... The PIBoost classifier [61] based on the ensemble method and costsensitivity scheme dealt with multi-class imbalanced data via a series of binary weaklearners and a margin-based exponential loss function ...

doi:10.3390/e23070822 fatcat:xxyob5zr6ffsbjjfgfy6a77y2u

DOAJ

In this paper we propose a novel time-space ensemble-based approach for the task of automatic music genre classification. ... by using several binary classifiers, are applied. ... The RR method converts a n-class problem into a series of binary problems, by creating a set of k = n(n − 1)/2 classifiers, one for each pair of classes. ...

doi:10.1007/11874850_38 fatcat:vx6rl2akcbap3ixq67xamd5moe

In this paper, a Multiple Learners-based Ensemble SMOTEBagging (ML-ESB) technique is proposed. ... The ML-ESB algorithm is a modified SMOTEBagging technique in which the ensemble of multiple instances of the single learner is replaced by multiple distinct classifiers. ... Oversample, the minority class Oversample the minority class instances using SMOTE ( , N, k) Step3: Train the classifier from Test new instances Step1: Pass new training instances to an ensemble of the ...

doi:10.14716/ijtech.v10i4.1743 fatcat:f44uvv7ahffupp52eabrhuaq7a

Given the training data and the class-set estimates of the m experts for a new instance, the multilabel classification problem is to estimate the true class set of that instance. ... This paper introduces a multi-label classification problem to the field of human computation. The problem involves training data such that each instance belongs to a set of classes. ... set Y x is substituted by a new output binary feature that equals 1 iff the class y of the binary classification problem BP y is in the set Y x . ...

dblp:conf/hcomp/SmirnovZPNI13a fatcat:xave27n6fjhtjg7ojcpxoiwfou

In this paper, we present a survey of many FCA-based approaches for classification by dividing them into methods based on a mono-classifier, methods based on ensemble classifiers and methods based on distributed ... Methods based on ensemble classifiers rely on the use of many classifiers. Among these methods, there are methods based on sequential training and methods based on parallel training. ... Methods based on ensemble classifiers There is a growing realization that the use of ensemble classifiers can be more effective than the use of single classifiers. ...

doi:10.3233/faia200762 fatcat:a4kasm3cxndxbogzree7mud5fe

Open Access

In this work we propose a class centric feature and classifier ensemble selection method which deviates from the conventional practice of employing a single, or an ensemble of classifiers trained with ... We adopt a binary decomposition technique to divide the multiclass problem into a set of binary problems which are then treated in a class specific manner. ... Therefore in this paper we focus on a nonhierarchical approach to class centric classification based on a binary decomposition technique. ...

doi:10.1007/978-3-642-34166-3_73 fatcat:rf3wj55qzzgo3inl3gz6mqecje

In this paper a novel, ensemble style, classification architecture is proposed as a solution to the multi-class classification problem. ... The idea is to use a non-rooted Directed Acyclic Graph (DAG) structure which holds a classifier at each node. ... Following only two branches also, of course, allows us to make comparisons with the operation of binary tree based hierarchical ensemble classifiers. ...

doi:10.1007/978-3-319-25032-8_3 dblp:conf/sgai/AlshdaifatCD15 fatcat:iqzhhi3u4fgydau6nr7ikls54a

This paper proposes a heuristic classifier ensemble to improve the performance of learning in multiclass problems. ... In this paper, some ensembles of classifiers are first created. The classifiers of each of these ensembles jointly work using majority weighting votes. ... In this new paradigm, a multiclass classifier in addition to a few ensembles of pairwise classifiers creates a classifier ensemble. ...

doi:10.1007/978-3-642-25085-9_45 fatcat:jvlehjxbizfqfmm2qjkdx5w62a

Racs based Weight Optimization and Layered Clustering-based ECOC

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An Ensemble of Classifiers Approach to Steganalysis

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A new artificial neural network ensemble based on feature selection and class recoding

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Intrusion detection using error correcting output code based ensemble

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A Novel Approach of Ensemble Learning with Feature Reduction for Classification of Binary and Multiclass IoT Data

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A Classifier Ensemble of Binary Classifier Ensembles

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Multi-Task Ensemble Creation for Advancing Performance of Image Segmentation

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Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

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Time-Space Ensemble Strategies for Automatic Music Genre Classification [chapter]

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Distinct Multiple Learner-Based Ensemble SMOTEBagging (ML-ESB) Method for Classification of Binary Class Imbalance Problems

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Aggregating Human-Expert Opinions for Multi-Label Classification

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Survey on Formal Concept Analysis Based Supervised Classification Techniques [chapter]

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A Class Centric Feature and Classifier Ensemble Selection Approach for Music Genre Classification [chapter]

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A Directed Acyclic Graph Based Approach to Multi-Class Ensemble Classification [chapter]

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A Scalable Heuristic Classifier for Huge Datasets: A Theoretical Approach [chapter]

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