Hubness-Aware Classification, Instance Selection and Feature Construction: Survey and Extensions to Time-Series

Tomašev, Nenad; Búza, Krisztián; Marussy, Kristóf; Kis, Piroska B.

doi:10.1007/978-3-662-45620-0_11

Cited by 34 publications

(25 citation statements)

References 38 publications

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“…The presentation of NHBNN and self-training is based on [21] and [11] respectively. Subsequently, we describe our proposed semisupervised approach in Section 2.3, which is followed by the methods used for the experimental evaluation in Section 2.4.…”

Section: Methodsmentioning

confidence: 99%

“…Even though (k, C)-occurrences are highly correlated, as shown in [21] and [23], NHBNN offers improvement over the basic kNN. This is in accordance with other results from the literature that state that Naive Bayes can deliver good results even in cases with high independence assumption violation [16].…”

Section: Nhbnn: Naive Hubness Bayesianmentioning

confidence: 99%

“…[5], [12], [13], [15], [22], [25], [26], [20] and [21] for a sur-vey. These algorithms try to recognize bad hubs and reduce their influence on classifications of unlabeled instances.…”

Section: Introductionmentioning

confidence: 99%

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Classification of gene expression data: A hubness-aware semi-supervised approach

Búza

2016

Computer Methods and Programs in Biomedicine

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Background and Objective Classification of gene expression data is the common denominator of various biomedical recognition tasks. However, obtaining class labels for large training samples may be difficult or even impossible in many cases. Therefore, semi-supervised classification techniques are required as semi-supervised classifiers take advantage of unlabeled data.Methods Gene expression data is high-dimensional which gives rise to the phenomena known under the umbrella of the curse of dimensionality, one of its recently explored aspects being the presence of hubs or hubness for short. Therefore, hubness-aware classifiers have been developed recently, such as Naive Hubness-Bayesian k-Nearest Neighbor (NHBNN).In this paper, we propose a semi-supervised extension of NHBNN which follows the self-training schema. As one of the core components of selftraining is the certainty score, we propose a new hubness-aware certainty score. ResultsWe performed experiments on publicly available gene expression data. These experiments show that the proposed classifier outperforms its competitors. We investigated the impact of each of the compo-1 nents (classification algorithm, semi-supervised technique, hubness-aware certainty score) separately and showed that each of these components are relevant to the performance of the proposed approach. ConclusionsOur results imply that our approach may increase classification accuracy and reduce computational costs (i.e., runtime). Based on the promising results presented in the paper, we envision that hubnessaware techniques will be used in various other biomedical machine learning tasks. In order to accelerate this process, we made an implementation of hubness-aware machine learning techniques publicly available in the PyHubs software package (http://www.biointelligence.hu/pyhubs) implemented in Python, one of the most popular programming languages of data science.

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Section: Methodsmentioning

confidence: 99%

Section: Nhbnn: Naive Hubness Bayesianmentioning

confidence: 99%

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Classification of gene expression data: A hubness-aware semi-supervised approach

Búza

2016

Computer Methods and Programs in Biomedicine

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“…Informally, this estimate can be interpreted as follows: we consider m additional pseudo-instances from each class and we assume that x i appears as one of the k-nearest neighbors of the pseudo-instances from class C. We use m = 1 in our experiments. Even though k-occurrences are highly correlated, as shown in [19] and [21], NHBNN offers improvement over the basic kNN. This is in accordance with other results from the literature that state that Naive Bayes can deliver good results even in cases with high independence assumption violation [15].…”

Section: Nhbnn: Naive Hubness Bayesian K-nearest Neighbormentioning

confidence: 99%

“…[4], [11], [12], [14], [20], [22], [23], and [19] for a survey. Hubs were observed in gene expression data [8], [14] and hubness was brought into relation with the performance of the SUCCESS semi-supervised time-series classifier [9], however, none of the aforementioned works focused on hubness-aware classifiers in semisupervised mode, i.e., when the classifier is allowed to learn both from labeled and unlabeled instances.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised Naive Hubness Bayesian k-Nearest Neighbor for Gene Expression Data

Búza

2016

Advances in Intelligent Systems and Computing

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Abstract. Classification of gene expression data is the common denominator of various biomedical recognition tasks. However, obtaining class labels for large training samples may be difficult or even impossible in many cases. Therefore, semi-supervised classification techniques are required as semi-supervised classifiers take advantage of the unlabeled data. Furthermore, gene expression data is high-dimensional which gives rise to the phenomena known under the umbrella of the curse of dimensionality, one of its recently explored aspects being the presence of hubs or hubness for short. Therefore, hubness-aware classifiers were developed recently, such as Hubness-Bayesian k-Nearest Neighbor (NHBNN). In this paper, we propose a semi-supervised extension of NHBNN and show in experiments on publicly available gene expression data that the proposed classifier outperforms all its examined competitors.

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Adaptive, Hubness-Aware Nearest Neighbour Classifier with Application to Hyperspectral Data

Romaszewski

Głomb

Cholewa

2018

Communications in Computer and Information Science

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Hubness-Aware Classification, Instance Selection and Feature Construction: Survey and Extensions to Time-Series

Cited by 34 publications

References 38 publications

Classification of gene expression data: A hubness-aware semi-supervised approach

Classification of gene expression data: A hubness-aware semi-supervised approach

Semi-supervised Naive Hubness Bayesian k-Nearest Neighbor for Gene Expression Data

Adaptive, Hubness-Aware Nearest Neighbour Classifier with Application to Hyperspectral Data

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