Cluster-based one-class ensemble for classification problems in information retrieval

Lipka, Nedim; Stein, Benno; Anderka, Maik

doi:10.1145/2348283.2348459

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…Wang et al exploit the inherent target data structures obtained via hierarchical clustering to create an ensemble of spherical 1‐class classifiers. Further work in using clustering for creating 1‐class classifier ensembles is conducted by Lipka et al, where the authors use the k ‐means algorithm to create an ensemble of OCSVM classifiers. A general‐purpose framework for using clustering for improving 1‐class classification is proposed by Krawczyk et al Their framework consists of 3 parts: the choice of clustering algorithm, the choice of 1‐class classifier, and the choice of how to combine decisions.…”

Section: Challenges In Imbalanced Domainsmentioning

confidence: 99%

Learning over subconcepts: Strategies for 1‐class classification

Sharma

Somayaji

Japkowicz

2017

Computational Intelligence

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In machine learning research and application, multiclass classification algorithms reign supreme. Their fundamental property is the reliance on the availability of data from all known categories to induce effective classifiers. Unfortunately, data from so‐called real‐world domains sometimes do not satisfy this property, and researchers use methods such as sampling to make the data more conducive for classification. However, there are scenarios in which even such explicit methods to rectify distributions fail. In such cases, 1‐class classification algorithms become the practical alternative. Unfortunately, domain complexity severely impacts their ability to produce effective classifiers. The work in this article addresses this issue and develops a strategy that allows for 1‐class classification over complex domains. In particular, we introduce the notion of learning along the lines of underlying domain concepts; an important source of complexity in domains is the presence of subconcepts, and by learning over them explicitly rather than on the entire domain as a whole, we can produce powerful 1‐class classification systems. The level of knowledge regarding these subconcepts will naturally vary by domain, and thus, we develop 3 distinct methodologies that take the amount of domain knowledge available into account. We demonstrate these over 3 real‐world domains.

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Section: Challenges In Imbalanced Domainsmentioning

confidence: 99%

Learning over subconcepts: Strategies for 1‐class classification

Sharma

Somayaji

Japkowicz

2017

Computational Intelligence

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“…It also exploits some additional kind of knowledgewhich classes are the most likely to be confused. Other authors also showed that clustering can be used to simply divide the problem into smaller ones solved independently by a separate classifiers [3,8]. In C k RBF, instead of splitting data and analyzing the output of several classifiers, we propose to include all the information gained through clustering into one, generic classifier.…”

Section: Related Workmentioning

confidence: 99%

Cluster based RBF Kernel for Support Vector Machines

Czarnecki,

Tabor

2014

Preprint

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In the classical Gaussian SVM classification we use the feature space projection transforming points to normal distributions with fixed covariance matrices (identity in the standard RBF and the covariance of the whole dataset in Mahalanobis RBF). In this paper we add additional information to Gaussian SVM by considering local geometry-dependent feature space projection. We emphasize that our approach is in fact an algorithm for a construction of the new Gaussiantype kernel.We show that better (compared to standard RBF and Mahalanobis RBF) classification results are obtained in the simple case when the space is preliminary divided by k-means into two sets and points are represented as normal distributions with a covariances calculated according to the dataset partitioning. We call the constructed method C k RBF, where k stands for the amount of clusters used in k-means. We show empirically on nine datasets from UCI repository that C 2 RBF increases the stability of the grid search (measured as the probability of finding good parameters).

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“…Clustering methods in information networks [1] become more and more popular in recent years. One can discover much interesting knowledge from the information networks by using appropriate clustering methods, and the clustering result can also be used in many fields such as information retrieval [2] and recommendation systems [3]. In particular, the real world information networks are often heterogeneous [4], which means in these networks objects and links between these objects may belong to different types.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised Clustering on Heterogeneous Information Networks

Luo

Pang

Wang

2014

Advances in Knowledge Discovery and Data Mining

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Abstract. Semi-supervised clustering on information networks combines both the labeled and unlabeled data sets with an aim to improve the clustering performance. However, the existing semi-supervised clustering methods are all designed for homogeneous networks and do not deal with heterogeneous ones. In this work, we propose a semi-supervised clustering approach to analyze heterogeneous information networks, which include multi-typed objects and links and may contain more useful semantic information. The major challenge in the clustering task here is how to handle multi-relations and diverse semantic meanings in heterogeneous networks. In order to deal with this challenge, we introduce the concept of relation-path to measure the similarity between two data objects of the same type. Thereafter, we make use of the labeled information to extract different weights for all relation-paths. Finally, we propose SemiRPClus, a complete framework for semi-supervised learning in heterogeneous networks. Experimental results demonstrate the distinct advantages in effectiveness and efficiency of our framework in comparison with the baseline and some state-of-the-art approaches.

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Cluster-based one-class ensemble for classification problems in information retrieval

Cited by 8 publications

References 10 publications

Learning over subconcepts: Strategies for 1‐class classification

Learning over subconcepts: Strategies for 1‐class classification

Cluster based RBF Kernel for Support Vector Machines

Semi-supervised Clustering on Heterogeneous Information Networks

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