scholarly journals Towards Enabling Binary Decomposition for Partial Label Learning

2018 ◽  
Author(s):  
Xuan Wu ◽  
Min-Ling Zhang
Keyword(s):  
Learning Algorithm ◽  
Experimental Studies ◽  
Ground Truth ◽  
Binary Classifier ◽  
Decomposition Approach ◽  
Novel Approach ◽  
Binary Decomposition ◽  
Binary Classifiers ◽  
Training Examples ◽  
Partial Label Learning

The task of partial label (PL) learning is to learn a multi-class classifier from training examples each associated with a set of candidate labels, among which only one corresponds to the ground-truth label. It is well known that for inducing multi-class predictive model, the most straightforward solution is binary decomposition which works by either one-vs-rest or one-vs-one strategy. Nonetheless, the ground-truth label for each PL training example is concealed in its candidate label set and thus not accessible to the learning algorithm, binary decomposition cannot be directly applied under partial label learning scenario. In this paper, a novel approach is proposed to solving partial label learning problem by adapting the popular one-vs-one decomposition strategy. Specifically, one binary classifier is derived for each pair of class labels, where PL training examples with distinct relevancy to the label pair are used to generate the corresponding binary training set. After that, one binary classifier is further derived for each class label by stacking over predictions of existing binary classifiers to improve generalization. Experimental studies on both artificial and real-world PL data sets clearly validate the effectiveness of the proposed binary decomposition approach w.r.t state-of-the-art partial label learning techniques.

scholarly journals Multi-View Partial Multi-Label Learning with Graph-Based Disambiguation

2020 ◽  
Vol 34 (04) ◽  
pp. 3553-3560 ◽  
Author(s):  
Ze-Sen Chen ◽  
Xuan Wu ◽  
Qing-Guo Chen ◽  
Yao Hu ◽  
Min-Ling Zhang
Keyword(s):  
Predictive Model ◽  
Clustering Analysis ◽  
Experimental Studies ◽  
Ground Truth ◽  
Two Stage ◽  
Feature Vectors ◽  
Similarity Graph ◽  
Training Examples ◽  
Guided Clustering

In multi-view multi-label learning (MVML), each training example is represented by different feature vectors and associated with multiple labels simultaneously. Nonetheless, the labeling quality of training examples is tend to be affected by annotation noises. In this paper, the problem of multi-view partial multi-label learning (MVPML) is studied, where the set of associated labels are assumed to be candidate ones and only partially valid. To solve the MVPML problem, a two-stage graph-based disambiguation approach is proposed. Firstly, the ground-truth labels of each training example are estimated by disambiguating the candidate labels with fused similarity graph. After that, the predictive model for each label is learned from embedding features generated from disambiguation-guided clustering analysis. Extensive experimental studies clearly validate the effectiveness of the proposed approach in solving the MVPML problem.

scholarly journals Partial Label Learning by Semantic Difference Maximization

2019 ◽  
Author(s):  
Lei Feng ◽  
Bo An
Keyword(s):  
State Of The Art ◽  
Ground Truth ◽  
Positive Answer ◽  
Learning Framework ◽  
Novel Approach ◽  
Semantic Difference ◽  
Weakly Supervised ◽  
Partial Label Learning ◽  
The Given ◽  
First Time

Partial label learning is a weakly supervised learning framework, in which each instance is provided with multiple candidate labels while only one of them is correct. Most of the existing approaches focus on leveraging the instance relationships to disambiguate the given noisy label space, while it is still unclear whether we can exploit potentially useful information in label space to alleviate the label ambiguities. This paper gives a positive answer to this question for the first time. Specifically, if two instances do not share any common candidate labels, they cannot have the same ground-truth label. By exploiting such dissimilarity relationships from label space, we propose a novel approach that aims to maximize the latent semantic differences of the two instances whose ground-truth labels are definitely different, while training the desired model simultaneously, thereby continually enlarging the gap of label confidences between two instances of different classes. Extensive experiments on artificial and real-world partial label datasets show that our approach significantly outperforms state-of-the-art counterparts.

scholarly journals Partial Label Learning Based on Fully Connected Deep Neural Network

2022 ◽  
Vol 16 ◽  
pp. 287-297
Author(s):  
Houjie Li ◽  
Lei Wu ◽  
Jianjun He ◽  
Ruirui Zheng ◽  
Yu Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Process Model ◽  
Deep Neural Network ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Ground Truth ◽  
Training Sample ◽  
Support Vector ◽  
Partial Label Learning ◽  
Fully Connected

The ambiguity of training samples in the partial label learning framework makes it difficult for us to develop learning algorithms and most of the existing algorithms are proposed based on the traditional shallow machine learn- ing models, such as decision tree, support vector machine, and Gaussian process model. Deep neu- ral networks have demonstrated excellent perfor- mance in many application fields, but currently it is rarely used for partial label learning frame- work. This study proposes a new partial label learning algorithm based on a fully connected deep neural network, in which the relationship between the candidate labels and the ground- truth label of each training sample is established by defining three new loss functions, and a regu- larization term is added to prevent overfitting. The experimental results on the controlled U- CI datasets and real-world partial label datasets reveal that the proposed algorithm can achieve higher classification accuracy than the state-of- the-art partial label learning algorithms.

scholarly journals Partial Label Learning via Label Enhancement

2019 ◽  
Vol 33 ◽  
pp. 5557-5564 ◽  
Author(s):  
Ning Xu ◽  
Jiaqi Lv ◽  
Xin Geng
Keyword(s):  
Predictive Model ◽  
Learning Strategy ◽  
Feature Space ◽  
Ground Truth ◽  
Learning Approaches ◽  
Topological Information ◽  
Common Strategy ◽  
Training Examples ◽  
Partial Label Learning ◽  
Label Distribution

Partial label learning aims to learn from training examples each associated with a set of candidate labels, among which only one label is valid for the training example. The common strategy to induce predictive model is trying to disambiguate the candidate label set, such as disambiguation by identifying the ground-truth label iteratively or disambiguation by treating each candidate label equally. Nonetheless, these strategies ignore considering the generalized label distribution corresponding to each instance since the generalized label distribution is not explicitly available in the training set. In this paper, a new partial label learning strategy named PL-LE is proposed to learn from partial label examples via label enhancement. Specifically, the generalized label distributions are recovered by leveraging the topological information of the feature space. After that, a multi-class predictive model is learned by fitting a regularized multi-output regressor with the generalized label distributions. Extensive experiments show that PL-LE performs favorably against state-ofthe-art partial label learning approaches.

Disambiguation Enabled Linear Discriminant Analysis for Partial Label Dimensionality Reduction

2022 ◽  
Vol 16 (4) ◽  
pp. 1-18
Author(s):  
Min-Ling Zhang ◽  
Jing-Han Wu ◽  
Wei-Xuan Bao
Keyword(s):  
Discriminant Analysis ◽  
Dimensionality Reduction ◽  
Linear Discriminant Analysis ◽  
Feature Space ◽  
Ground Truth ◽  
Learning System ◽  
Generalization Performance ◽  
Linear Discriminant ◽  
Training Examples ◽  
Partial Label Learning

As an emerging weakly supervised learning framework, partial label learning considers inaccurate supervision where each training example is associated with multiple candidate labels among which only one is valid. In this article, a first attempt toward employing dimensionality reduction to help improve the generalization performance of partial label learning system is investigated. Specifically, the popular linear discriminant analysis (LDA) techniques are endowed with the ability of dealing with partial label training examples. To tackle the challenge of unknown ground-truth labeling information, a novel learning approach named Delin is proposed which alternates between LDA dimensionality reduction and candidate label disambiguation based on estimated labeling confidences over candidate labels. On one hand, the (kernelized) projection matrix of LDA is optimized by utilizing disambiguation-guided labeling confidences. On the other hand, the labeling confidences are disambiguated by resorting to k NN aggregation in the LDA-induced feature space. Extensive experiments over a broad range of partial label datasets clearly validate the effectiveness of Delin in improving the generalization performance of well-established partial label learning algorithms.

scholarly journals A Random Forest Model for the Prediction of FOG Content in Inlet Wastewater from Urban WWTPs

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1237
Author(s):  
Vanesa Mateo Pérez ◽  
José Manuel Mesa Fernández ◽  
Joaquín Villanueva Balsera ◽  
Cristina Alonso Álvarez
Keyword(s):  
Random Forest ◽  
Wastewater Treatment Plants ◽  
Learning Algorithm ◽  
Global Improvement ◽  
Treatment Processes ◽  
Early Adoption ◽  
Novel Approach ◽  
Number Of Factors ◽  
Initial Stage ◽  
Correct Design

The content of fats, oils, and greases (FOG) in wastewater, as a result of food preparation, both in homes and in different commercial and industrial activities, is a growing problem. In addition to the blockages generated in the sanitary networks, it also represents a difficulty for the performance of wastewater treatment plants (WWTP), increasing energy and maintenance costs and worsening the performance of downstream treatment processes. The pretreatment stage of these facilities is responsible for removing most of the FOG to avoid these problems. However, so far, optimization has been limited to the correct design and initial installation dimensioning. Proper management of this initial stage is left to the experience of the operators to adjust the process when changes occur in the characteristics of the wastewater inlet. The main difficulty is the large number of factors influencing these changes. In this work, a prediction model of the FOG content in the inlet water is presented. The model is capable of correctly predicting 98.45% of the cases in training and 72.73% in testing, with a relative error of 10%. It was developed using random forest (RF) and the good results obtained (R2 = 0.9348 and RMSE = 0.089 in test) will make it possible to improve operations in this initial stage. The good features of this machine learning algorithm had not been used, so far, in the modeling of pretreatment parameters. This novel approach will result in a global improvement in the performance of this type of facility allowing early adoption of adjustments to the pretreatment process to remove the maximum amount of FOG.

EXPRESS: When Algorithms Fail: Consumers’ Responses to Brand Harm Crises Caused by Algorithm Errors

2021 ◽  
pp. 002224292199708
Author(s):  
Raji Srinivasan ◽  
Gülen Sarial-Abi
Keyword(s):  
Machine Learning ◽  
Theory Of Mind ◽  
Human Error ◽  
Learning Algorithm ◽  
Experimental Studies ◽  
Task Characteristics ◽  
Machine Learning Algorithm ◽  
Incentive Compatible ◽  
Mind Perception ◽  
Interactive Task

Algorithms increasingly used by brands sometimes fail to perform as expected or even worse, cause harm, causing brand harm crises. Unfortunately, algorithm failures are increasing in frequency. Yet, we know little about consumers’ responses to brands following such brand harm crises. Extending developments in the theory of mind perception, we hypothesize that following a brand harm crisis caused by an algorithm error (vs. human error), consumers will respond less negatively to the brand. We further hypothesize that consumers’ lower mind perception of agency of the algorithm (vs. human) for the error that lowers their perceptions of the algorithm’s responsibility for the harm caused by the error will mediate this relationship. We also hypothesize four moderators of this relationship: two algorithm characteristics, anthropomorphized algorithm and machine learning algorithm and two task characteristics where the algorithm is deployed, subjective (vs. objective) task and interactive (vs. non-interactive) task. We find support for the hypotheses in eight experimental studies including two incentive-compatible studies. We examine the effects of two managerial interventions to manage the aftermath of brand harm crises caused by algorithm errors. The research’s findings advance the literature on brand harm crises, algorithm usage, and algorithmic marketing and generate managerial guidelines to address the aftermath of such brand harm crises.

scholarly journals Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Peter M. Maloca ◽  
Philipp L. Müller ◽  
Aaron Y. Lee ◽  
Adnan Tufail ◽  
Konstantinos Balaskas ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Optical Coherence Tomography ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Learning Algorithm ◽  
Ground Truth ◽  
Optical Coherence Tomography Image ◽  
Optical Coherence ◽  
Tomography Image

AbstractMachine learning has greatly facilitated the analysis of medical data, while the internal operations usually remain intransparent. To better comprehend these opaque procedures, a convolutional neural network for optical coherence tomography image segmentation was enhanced with a Traceable Relevance Explainability (T-REX) technique. The proposed application was based on three components: ground truth generation by multiple graders, calculation of Hamming distances among graders and the machine learning algorithm, as well as a smart data visualization (‘neural recording’). An overall average variability of 1.75% between the human graders and the algorithm was found, slightly minor to 2.02% among human graders. The ambiguity in ground truth had noteworthy impact on machine learning results, which could be visualized. The convolutional neural network balanced between graders and allowed for modifiable predictions dependent on the compartment. Using the proposed T-REX setup, machine learning processes could be rendered more transparent and understandable, possibly leading to optimized applications.

scholarly journals Pros and Cons of the Cannabinoid System in Cancer: Focus on Hematological Malignancies

Molecules ◽  
2021 ◽  
Vol 26 (13) ◽  
pp. 3866
Author(s):  
Natasha Irrera ◽  
Alessandra Bitto ◽  
Emanuela Sant’Antonio ◽  
Rita Lauro ◽  
Caterina Musolino ◽  
...  
Keyword(s):  
Cannabinoid Receptors ◽  
Hematological Malignancies ◽  
Antitumor Agents ◽  
Control Cell ◽  
Experimental Studies ◽  
Endocannabinoid System ◽  
Chemotherapeutic Agents ◽  
Cancer Cell Growth ◽  
Cell Functions ◽  
Novel Approach

The endocannabinoid system (ECS) is a composite cell-signaling system that allows endogenous cannabinoid ligands to control cell functions through the interaction with cannabinoid receptors. Modifications of the ECS might contribute to the pathogenesis of different diseases, including cancers. However, the use of these compounds as antitumor agents remains debatable. Pre-clinical experimental studies have shown that cannabinoids (CBs) might be effective for the treatment of hematological malignancies, such as leukemia and lymphoma. Specifically, CBs may activate programmed cell death mechanisms, thus blocking cancer cell growth, and may modulate both autophagy and angiogenesis. Therefore, CBs may have significant anti-tumor effects in hematologic diseases and may synergistically act with chemotherapeutic agents, possibly also reducing chemoresistance. Moreover, targeting ECS might be considered as a novel approach for the management of graft versus host disease, thus reducing some symptoms such as anorexia, cachexia, fatigue, anxiety, depression, and neuropathic pain. The aim of the present review is to collect the state of the art of CBs effects on hematological tumors, thus focusing on the essential topics that might be useful before moving into the clinical practice.

Hypoxic mechanisms in primary headaches

Cephalalgia ◽  
2016 ◽  
Vol 37 (4) ◽  
pp. 372-384 ◽  
Author(s):  
Josefine Britze ◽  
Nanna Arngrim ◽  
Henrik Winther Schytz ◽  
Messoud Ashina
Keyword(s):  
Cluster Headache ◽  
High Altitude ◽  
Acute Mountain Sickness ◽  
Experimental Studies ◽  
Epidemiological Studies ◽  
Primary Headaches ◽  
Secondary Headaches ◽  
Novel Approach ◽  
Calcitonin Gene ◽  
Level Of Knowledge

Background Hypoxia causes secondary headaches such as high-altitude headache (HAH) and headache due to acute mountain sickness. These secondary headaches mimic primary headaches such as migraine, which suggests a common link. We review and discuss the possible role of hypoxia in migraine and cluster headache. Methods This narrative review investigates the current level of knowledge on the relation of hypoxia in migraine and cluster headache based on epidemiological and experimental studies. Findings Epidemiological studies suggest that living in high-altitude areas increases the risk of migraine and especially migraine with aura. Human provocation models show that hypoxia provokes migraine with and without aura, whereas cluster headache has not been reliably induced by hypoxia. Possible pathophysiological mechanisms include hypoxia-induced release of nitric oxide and calcitonin gene-related peptide, cortical spreading depression and leakage of the blood-brain barrier. Conclusion There is a possible link between hypoxia and migraine and maybe cluster headache, but the exact mechanism is currently unknown. Provocation models of hypoxia have yielded interesting results suggesting a novel approach to study in depth the mechanism underlying hypoxia and primary headaches.

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