Protein Classification with Multiple Algorithms

Diplaris, Sotiris; Tsoumakas, Grigorios; Mitkas, Pericles A.; Vlahavas, Ioannis

doi:10.1007/11573036_42

Cited by 167 publications

(89 citation statements)

References 17 publications

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“…The yeast [29] dataset concerns the problem of assigning functional classes to genes of the Saccharomyces cerevisiae genome. The genbase [30] dataset represents the problem of assigning classes to proteins based on detected motifs that serve as input features.…”

Section: Datasetsmentioning

confidence: 99%

How Is a Data-Driven Approach Better than Random Choice in Label Space Division for Multi-Label Classification?

Szymański

Kajdanowicz

Kersting

2016

Entropy

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Abstract:We propose using five data-driven community detection approaches from social networks to partition the label space in the task of multi-label classification as an alternative to random partitioning into equal subsets as performed by RAkELd. We evaluate modularity-maximizing using fast greedy and leading eigenvector approximations, infomap, walktrap and label propagation algorithms. For this purpose, we propose to construct a label co-occurrence graph (both weighted and unweighted versions) based on training data and perform community detection to partition the label set. Then, each partition constitutes a label space for separate multi-label classification sub-problems. As a result, we obtain an ensemble of multi-label classifiers that jointly covers the whole label space. Based on the binary relevance and label powerset classification methods, we compare community detection methods to label space divisions against random baselines on 12 benchmark datasets over five evaluation measures. We discover that data-driven approaches are more efficient and more likely to outperform RAkELd than binary relevance or label powerset is, in every evaluated measure. For all measures, apart from Hamming loss, data-driven approaches are significantly better than RAkELd (α = 0.05), and at least one data-driven approach is more likely to outperform RAkELd than a priori methods in the case of RAkELd's best performance. This is the largest RAkELd evaluation published to date with 250 samplings per value for 10 values of RAkELd parameter k on 12 datasets published to date.

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Section: Datasetsmentioning

confidence: 99%

How Is a Data-Driven Approach Better than Random Choice in Label Space Division for Multi-Label Classification?

Szymański

Kajdanowicz

Kersting

2016

Entropy

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“…Five datasets-Bibtex, Delicious, Enron, Language Log (LLog) and Slashdot-were obtained from the application of text categorization [10][11][12]30]; the Corel5K dataset was obtained from annotated images, each containing multiple objects [52]. Two datasets-Genbase and Yeast-were obtained by representing the multiple classes of biological functions [2,53]. These datasets have frequently been employed for the purpose of comparison in multi-label feature selection studies [15,18,35].…”

Section: Datasets and Experimental Settingsmentioning

confidence: 99%

“…Examples include taxonomies of email corpuses from texts or the emotive qualities of music from audio sources [1][2][3][4]. This technique is useful for learning a model when input patterns can be associated with multiple labels concurrently [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection

Lee

Kim

2016

Entropy

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Multi-label feature selection is designed to select a subset of features according to their importance to multiple labels. This task can be achieved by ranking the dependencies of features and selecting the features with the highest rankings. In a multi-label feature selection problem, the algorithm may be faced with a dataset containing a large number of labels. Because the computational cost of multi-label feature selection increases according to the number of labels, the algorithm may suffer from a degradation in performance when processing very large datasets. In this study, we propose an efficient multi-label feature selection method based on an information-theoretic label selection strategy. By identifying a subset of labels that significantly influence the importance of features, the proposed method efficiently outputs a feature subset. Experimental results demonstrate that the proposed method can identify a feature subset much faster than conventional multi-label feature selection methods for large multi-label datasets.

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“…We evaluate the performance of label compression and recovery, and multi-label prediction of CL on 21 datasets from different domains and of different scales, including Bibtex ), Corel5k (Duygulu et al 2002), Mediamill (Snoek et al 2006), IMDB (Read 2010), Enron (Tsoumakas 2010), Genbase (Diplaris et al 2005), Medical (Tsoumakas 2010), Emotions (Trohidis et al 2008), Scene (Boutell et al 2004), Slashdot (Read 2010) and 11 sub datasets included in Yahoo dataset (Ueda and Saito 2002). These datasets are collected from different practical problems such as text classification, image annotation, scene classification, music categorization, genomics and web page classification.…”

Section: Datasetsmentioning

confidence: 99%

Compressed labeling on distilled labelsets for multi-label learning

Zhou

Tao

2012

Mach Learn

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Directly applying single-label classification methods to the multi-label learning problems substantially limits both the performance and speed due to the imbalance, dependence and high dimensionality of the given label matrix. Existing methods either ignore these three problems or reduce one with the price of aggravating another. In this paper, we propose a {0, 1} label matrix compression and recovery method termed "compressed labeling (CL)" to simultaneously solve or at least reduce these three problems. CL first compresses the original label matrix to improve balance and independence by preserving the signs of its Gaussian random projections. Afterward, we directly utilize popular binary classification methods (e.g., support vector machines) for each new label. A fast recovery algorithm is developed to recover the original labels from the predicted new labels. In the recovery algorithm, a "labelset distilling method" is designed to extract distilled labelsets (DLs), i.e., the frequently appeared label subsets from the original labels via recursive clustering and subtraction. Given a distilled and an original label vector, we discover that the signs of their random projections have an explicit joint distribution that can be quickly computed from a geometric inference. Based on this observation, the original label vector is exactly determined after performing a series of Kullback-Leibler divergence based hypothesis tests on the distribution about the new labels. CL significantly improves the balance Mach Learn (2012) 88:69-126 of the training samples and reduces the dependence between different labels. Moreover, it accelerates the learning process by training fewer binary classifiers for compressed labels, and makes use of label dependence via DLs based tests. Theoretically, we prove the recovery bounds of CL which verifies the effectiveness of CL for label compression and multi-label classification performance improvement brought by label correlations preserved in DLs. We show the effectiveness, efficiency and robustness of CL via 5 groups of experiments on 21 datasets from text classification, image annotation, scene classification, music categorization, genomics and web page classification.

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Protein Classification with Multiple Algorithms

Cited by 167 publications

References 17 publications

How Is a Data-Driven Approach Better than Random Choice in Label Space Division for Multi-Label Classification?

How Is a Data-Driven Approach Better than Random Choice in Label Space Division for Multi-Label Classification?

Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection

Compressed labeling on distilled labelsets for multi-label learning

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