scholarly journals Towards Automated Semi-Supervised Learning

2019 ◽  
Vol 33 ◽  
pp. 4237-4244 ◽  
Author(s):  
Yu-Feng Li ◽  
Hai Wang ◽  
Tong Wei ◽  
Wei-Wei Tu
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Semisupervised Learning ◽  
Learning System ◽  
Large Margin ◽  
Automated Learning ◽  
Machine Learning Model ◽  
Meta Learning ◽  
Performance Deterioration ◽  
Automated Machine Learning

Automated Machine Learning (AutoML) aims to build an appropriate machine learning model for any unseen dataset automatically, i.e., without human intervention. Great efforts have been devoted on AutoML while they typically focus on supervised learning. In many applications, however, semisupervised learning (SSL) are widespread and current AutoML systems could not well address SSL problems. In this paper, we propose to present an automated learning system for SSL (AUTO-SSL). First, meta-learning with enhanced meta-features is employed to quickly suggest some instantiations of the SSL techniques which are likely to perform quite well. Second, a large margin separation method is proposed to fine-tune the hyperparameters and more importantly, alleviate performance deterioration. The basic idea is that, if a certain hyperparameter owns a high quality, its predictive results on unlabeled data may have a large margin separation. Extensive empirical results over 200 cases demonstrate that our proposal on one side achieves highly competitive or better performance compared to the state-of-the-art AutoML system AUTO-SKLEARN and classical SSL techniques, on the other side unlike classical SSL techniques which often significantly degenerate performance, our proposal seldom suffers from such deficiency.

scholarly journals AutoDAL: Distributed Active Learning with Automatic Hyperparameter Selection

2020 ◽  
Vol 34 (04) ◽  
pp. 3537-3544
Author(s):  
Xu Chen ◽  
Brett Wujek
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Supervised Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Learning System ◽  
Automated Learning ◽  
Benchmark Datasets ◽  
Hyperparameter Selection ◽  
Query Selection

Automated machine learning (AutoML) strives to establish an appropriate machine learning model for any dataset automatically with minimal human intervention. Although extensive research has been conducted on AutoML, most of it has focused on supervised learning. Research of automated semi-supervised learning and active learning algorithms is still limited. Implementation becomes more challenging when the algorithm is designed for a distributed computing environment. With this as motivation, we propose a novel automated learning system for distributed active learning (AutoDAL) to address these challenges. First, automated graph-based semi-supervised learning is conducted by aggregating the proposed cost functions from different compute nodes in a distributed manner. Subsequently, automated active learning is addressed by jointly optimizing hyperparameters in both the classification and query selection stages leveraging the graph loss minimization and entropy regularization. Moreover, we propose an efficient distributed active learning algorithm which is scalable for big data by first partitioning the unlabeled data and replicating the labeled data to different worker nodes in the classification stage, and then aggregating the data in the controller in the query selection stage. The proposed AutoDAL algorithm is applied to multiple benchmark datasets and a real-world electrocardiogram (ECG) dataset for classification. We demonstrate that the proposed AutoDAL algorithm is capable of achieving significantly better performance compared to several state-of-the-art AutoML approaches and active learning algorithms.

scholarly journals Testing the Suitability of Automated Machine Learning for Weeds Identification

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 34-47
Author(s):  
Borja Espejo-Garcia ◽  
Ioannis Malounas ◽  
Eleanna Vali ◽  
Spyros Fountas
Keyword(s):  
Machine Learning ◽  
Plant Protection ◽  
Crop Protection ◽  
Identification Problem ◽  
Learning System ◽  
Classifier Ensembles ◽  
Automated Machine Learning ◽  
A New Technique ◽  
Plant Seedlings ◽  
And Training

In the past years, several machine-learning-based techniques have arisen for providing effective crop protection. For instance, deep neural networks have been used to identify different types of weeds under different real-world conditions. However, these techniques usually require extensive involvement of experts working iteratively in the development of the most suitable machine learning system. To support this task and save resources, a new technique called Automated Machine Learning has started being studied. In this work, a complete open-source Automated Machine Learning system was evaluated with two different datasets, (i) The Early Crop Weeds dataset and (ii) the Plant Seedlings dataset, covering the weeds identification problem. Different configurations, such as the use of plant segmentation, the use of classifier ensembles instead of Softmax and training with noisy data, have been compared. The results showed promising performances of 93.8% and 90.74% F1 score depending on the dataset used. These performances were aligned with other related works in AutoML, but they are far from machine-learning-based systems manually fine-tuned by human experts. From these results, it can be concluded that finding a balance between manual expert work and Automated Machine Learning will be an interesting path to work in order to increase the efficiency in plant protection.

scholarly journals Novel Meta-Features for Automated Machine Learning Model Selection in Anomaly Detection

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Milos Kotlar ◽  
Marija Punt ◽  
Zaharije Radivojevic ◽  
Milos Cvetanovic ◽  
Veljko Milutinovic
Keyword(s):  
Machine Learning ◽  
Model Selection ◽  
Anomaly Detection ◽  
Learning Model ◽  
Machine Learning Model ◽  
Automated Machine Learning

Cost-sensitive meta-learning framework

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Samar Ali Shilbayeh ◽  
Sunil Vadera
Keyword(s):  
Machine Learning ◽  
Learning System ◽  
Classification Algorithms ◽  
Data Sets ◽  
Classification Methods ◽  
Content Type ◽  
Learning Framework ◽  
Cost Sensitive Classification ◽  
Meta Learning ◽  
Training Examples

Purpose This paper aims to describe the use of a meta-learning framework for recommending cost-sensitive classification methods with the aim of answering an important question that arises in machine learning, namely, “Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?” Design/methodology/approach This paper describes the use of a meta-learning framework for recommending cost-sensitive classification methods for the aim of answering an important question that arises in machine learning, namely, “Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?” The framework is based on the idea of applying machine learning techniques to discover knowledge about the performance of different machine learning algorithms. It includes components that repeatedly apply different classification methods on data sets and measures their performance. The characteristics of the data sets, combined with the algorithms and the performance provide the training examples. A decision tree algorithm is applied to the training examples to induce the knowledge, which can then be used to recommend algorithms for new data sets. The paper makes a contribution to both meta-learning and cost-sensitive machine learning approaches. Those both fields are not new, however, building a recommender that recommends the optimal case-sensitive approach for a given data problem is the contribution. The proposed solution is implemented in WEKA and evaluated by applying it on different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system. The developed solution takes the misclassification cost into consideration during the learning process, which is not available in the compared project. Findings The proposed solution is implemented in WEKA and evaluated by applying it to different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system. Originality/value The paper presents a major piece of new information in writing for the first time. Meta-learning work has been done before but this paper presents a new meta-learning framework that is costs sensitive.

scholarly journals Automatic subtyping of individuals with Primary Progressive Aphasia

2020 ◽  
Author(s):  
Charalambos Themistocleous ◽  
Bronte Ficek ◽  
Kimberly Webster ◽  
Dirk-Bart den Ouden ◽  
Argye E. Hillis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Primary Progressive Aphasia ◽  
Support Vector ◽  
Progressive Aphasia ◽  
Primary Progressive ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Automated Machine Learning

AbstractBackgroundThe classification of patients with Primary Progressive Aphasia (PPA) into variants is time-consuming, costly, and requires combined expertise by clinical neurologists, neuropsychologists, speech pathologists, and radiologists.ObjectiveThe aim of the present study is to determine whether acoustic and linguistic variables provide accurate classification of PPA patients into one of three variants: nonfluent PPA, semantic PPA, and logopenic PPA.MethodsIn this paper, we present a machine learning model based on Deep Neural Networks (DNN) for the subtyping of patients with PPA into three main variants, using combined acoustic and linguistic information elicited automatically via acoustic and linguistic analysis. The performance of the DNN was compared to the classification accuracy of Random Forests, Support Vector Machines, and Decision Trees, as well as expert clinicians’ classifications.ResultsThe DNN model outperformed the other machine learning models with 80% classification accuracy, providing reliable subtyping of patients with PPA into variants and it even outperformed auditory classification of patients into variants by clinicians.ConclusionsWe show that the combined speech and language markers from connected speech productions provide information about symptoms and variant subtyping in PPA. The end-to-end automated machine learning approach we present can enable clinicians and researchers to provide an easy, quick and inexpensive classification of patients with PPA.

scholarly journals PASSer2.0: Accurate Prediction of Protein Allosteric Sites Through Automated Machine Learning

2021 ◽  
Author(s):  
Sian Xiao ◽  
Hao Tian ◽  
Peng Tao
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Computational Model ◽  
Protein Dynamics ◽  
Allosteric Site ◽  
Multiple Indicators ◽  
Machine Learning Model ◽  
Fundamental Process ◽  
Allosteric Sites ◽  
Automated Machine Learning

Allostery is a fundamental process in regulating proteins’ activity. The discovery, design and development of allosteric drugs demand for better identification of allosteric sites. Several computational methods have been developed previously to predict allosteric sites using static pocket features and protein dynamics. Here, we present a computational model using automated machine learning for allosteric site prediction. Our model, PASSer2.0, advanced the previous results and performed well across multiple indicators with 89.2% of allosteric pockets appeared among the top 3 positions. The trained machine learning model has been integrated with the Protein Allosteric Sites Server (https://passer.smu.edu) to facilitate allosteric drug discovery.

scholarly journals COVID-Classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest X-ray images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Abolfazl Zargari Khuzani ◽  
Morteza Heidari ◽  
S. Ali Shariati
Keyword(s):  
Machine Learning ◽  
Optimal Allocation ◽  
Global Features ◽  
X Ray ◽  
Learning Classifier ◽  
Machine Learning Model ◽  
Chest X Ray ◽  
Hospital Resources ◽  
Efficient Machine ◽  
Automated Machine Learning

AbstractChest-X ray (CXR) radiography can be used as a first-line triage process for non-COVID-19 patients with pneumonia. However, the similarity between features of CXR images of COVID-19 and pneumonia caused by other infections makes the differential diagnosis by radiologists challenging. We hypothesized that machine learning-based classifiers can reliably distinguish the CXR images of COVID-19 patients from other forms of pneumonia. We used a dimensionality reduction method to generate a set of optimal features of CXR images to build an efficient machine learning classifier that can distinguish COVID-19 cases from non-COVID-19 cases with high accuracy and sensitivity. By using global features of the whole CXR images, we successfully implemented our classifier using a relatively small dataset of CXR images. We propose that our COVID-Classifier can be used in conjunction with other tests for optimal allocation of hospital resources by rapid triage of non-COVID-19 cases.

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