Data-Dependent Generalization Bounds for Multi-Class Classification

Lei, Yunwen; Doǧan, Ürün; Kloft, Marius

doi:10.1109/tit.2019.2893916

Cited by 38 publications

(37 citation statements)

References 51 publications

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“…The precision is the ratio of the true positive results to all positive results [49], and the mean value of the precision of each sample label is the macro-precision. The recall represents the number of positive results that were correctly classified as positive in the experimental result [48], and the mean value of the recall of each sample label is the macro-recall. The macro-F1 score is the harmonic mean of the macro-precision and macro-recall.…”

Section: Comparison Of the Classification Results Of The Four Methodsmentioning

confidence: 99%

“…The experimental results of the four methods are summarized in Table 5 ; we used four evaluation indicators (accuracy, macro-Precision, macro-recall, and macro-F1 score) to assess the classification performance of the models. The accuracy represents the ratio of the correctly classified samples to the total samples [ 48 ]. The precision is the ratio of the true positive results to all positive results [ 49 ], and the mean value of the precision of each sample label is the macro-precision.…”

Section: Resultsmentioning

confidence: 99%

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A Machine Learning Method for the Detection of Brown Core in the Chinese Pear Variety Huangguan Using a MOS-Based E-Nose

Wei

2020

Sensors

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The brown core is an internal disorder that significantly affects the palatability and economic value of Chinese pears. In this study, a framework that includes a back-propagation neural network (BPNN) and extreme learning machine (ELM) (BP-ELMNN) was proposed for the detection of brown core in the Chinese pear variety Huangguan. The odor data of pear were collected using a metal oxide semiconductor (MOS) electronic nose (E-nose). Principal component analysis was used to analyze the complexity of the odor emitted by pears with brown cores. The performances of several machine learning algorithms, i.e., radial basis function neural network (RBFNN), BPNN, and ELM, were compared with that of the BP-ELMNN. The experimental results showed that the proposed framework provided the best results for the test samples, with an accuracy of 0.9683, a macro-precision of 0.9688, a macro-recall of 0.9683, and a macro-F1 score of 0.9685. The results demonstrate that the use of machine learning algorithms for the analysis of E-nose data is a feasible and non-destructive method to detect brown core in pears.

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Section: Comparison Of the Classification Results Of The Four Methodsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A Machine Learning Method for the Detection of Brown Core in the Chinese Pear Variety Huangguan Using a MOS-Based E-Nose

Wei

2020

Sensors

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“…For multi-class classification, the theory of extreme classifcation has been developed in the recent work [25]. In similar context, the behavior of tail-labels for flat and classification with taxonomies has been studied in the previous work [4,2,5,3,6].…”

Section: Predictive Performance 5 Related Workmentioning

confidence: 99%

Data scarcity, robustness and extreme multi-label classification

Babbar

Schölkopf

2019

Mach Learn

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The goal in extreme multi-label classification is to learn a classifier which can assign a small subset of relevant labels to an instance from an extremely large set of target labels. Datasets in extreme classification exhibit a long tail of labels which have small number of positive training instances. In this work, we pose the learning task in extreme classification with large number of tail-labels as learning in the presence of adversarial perturbations. This view motivates a robust optimization framework and equivalence to a corresponding regularized objective.Under the proposed robustness framework, we demonstrate efficacy of Hamming loss function for taillabel detection in extreme classification. The equivalent regularized objective, in combination with proximal gradient based optimization, performs better than state-ofthe-art methods on propensity scored versions of preci-sion@k and nDCG@k(upto 20% relative improvement over PFastreXML -a leading tree-based approach and 60% relative improvement over SLEEC -a leading labelembedding approach). Furthermore, we also highlight the sub-optimality of a sparse solver in a widely used package for large-scale linear classification, which is interesting in its own right. We also investigate the spectral properties of label graphs for providing novel insights towards understanding the conditions governing the performance of Hamming loss based one-vs-rest scheme vis-à-vis label embedding methods.

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“…But these systems are completely different. A multiclass classification system aims to classify documents into a single class over multiple (i.e., more than two) classes [ 3 ]. On the other hand, multilabel systems assign one or more classes to a particular document [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…In multilabel systems, the more classes there are dependent on each other, the more efficient it is to assign multiple classes to a particular document [ 4 ], whereas, in a multiclass problem, each document must preserve unique features of a particular class rather than share them with other classes. It is hard to ensure label independence in that case, which leads to a negative impact on the performance side of the classification system [ 3 ]. Eventually, all these problems may be partially or completely solvable by enhancing feature representation methods.…”

Section: Introductionmentioning

confidence: 99%

Classification of Biomedical Texts for Cardiovascular Diseases with Deep Neural Network Using a Weighted Feature Representation Method

2020

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This study aims to improve the performance of multiclass classification of biomedical texts for cardiovascular diseases by combining two different feature representation methods, i.e., bag-of-words (BoW) and word embeddings (WE). To hybridize the two feature representations, we investigated a set of possible statistical weighting schemes to combine with each element of WE vectors, which were term frequency (TF), inverse document frequency (IDF) and class probability (CP) methods. Thus, we built a multiclass classification model using a bidirectional long short-term memory (BLSTM) with deep neural networks for all investigated operations of feature vector combinations. We used MIMIC III and the PubMed dataset for the developing language model. To evaluate the performance of our weighted feature representation approaches, we conducted a set of experiments for examining multiclass classification performance with the deep neural network model and other state-of-the-art machine learning (ML) approaches. In all experiments, we used the OHSUMED-400 dataset, which includes PubMed abstracts related with specifically one class over 23 cardiovascular disease categories. Afterwards, we presented the results obtained from experiments and provided a comparison with related research in the literature. The results of the experiment showed that our BLSTM model with the weighting techniques outperformed the baseline and other machine learning approaches in terms of validation accuracy. Finally, our model outperformed the scores of related studies in the literature. This study shows that weighted feature representation improves the performance of the multiclass classification.

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Data-Dependent Generalization Bounds for Multi-Class Classification

Cited by 38 publications

References 51 publications

A Machine Learning Method for the Detection of Brown Core in the Chinese Pear Variety Huangguan Using a MOS-Based E-Nose

A Machine Learning Method for the Detection of Brown Core in the Chinese Pear Variety Huangguan Using a MOS-Based E-Nose

Data scarcity, robustness and extreme multi-label classification

Classification of Biomedical Texts for Cardiovascular Diseases with Deep Neural Network Using a Weighted Feature Representation Method

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