A novel meta-learning framework: Multi-features adaptive aggregation method with information enhancer

2021 ◽  
Author(s):  
Hailiang Ye ◽  
Yi Wang ◽  
Feilong Cao
Keyword(s):  
Aggregation Method ◽  
Learning Framework ◽  
Adaptive Aggregation ◽  
Meta Learning

Multi-polarization Fusion Few-shot HRRP Target Recognition Based on Meta-learning Framework

2021 ◽  
pp. 1-1
Author(s):  
Qi Liu ◽  
Xinyu Zhang ◽  
Yongxiang Liu ◽  
Kai Huo ◽  
Weidong Jiang ◽  
...  
Keyword(s):  
Target Recognition ◽  
Learning Framework ◽  
Meta 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.

Improving Techniques for Naïve Bayes Text Classifiers

2010 ◽  
pp. 111-127
Author(s):  
Han-joon Kim
Keyword(s):  
Text Classification ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Systems ◽  
Classification Model ◽  
Learning Approaches ◽  
Learning Framework ◽  
The Em Algorithm ◽  
Meta Learning ◽  
Text Classifiers

This chapter introduces two practical techniques for improving Naïve Bayes text classifiers that are widely used for text classification. The Naïve Bayes has been evaluated to be a practical text classification algorithm due to its simple classification model, reasonable classification accuracy, and easy update of classification model. Thus, many researchers have a strong incentive to improve the Naïve Bayes by combining it with other meta-learning approaches such as EM (Expectation Maximization) and Boosting. The EM approach is to combine the Naïve Bayes with the EM algorithm and the Boosting approach is to use the Naïve Bayes as a base classifier in the AdaBoost algorithm. For both approaches, a special uncertainty measure fit for Naïve Bayes learning is used. In the Naïve Bayes learning framework, these approaches are expected to be practical solutions to the problem of lack of training documents in text classification systems.

scholarly journals Deep Meta-Learning Energy-Aware Path Planner for Unmanned Ground Vehicles in Unknown Terrains

2021 ◽  
Author(s):  
Marco Visca
Keyword(s):  
Energy Model ◽  
Ground Vehicles ◽  
Energy Aware ◽  
Ieee Transaction ◽  
Learning Framework ◽  
Planning Framework ◽  
Meta Learning ◽  
Dynamic Simulator ◽  
Adaptive Heuristic ◽  
Path Planner

This paper presents an adaptive energy-aware prediction and planning framework for vehicles navigating over terrains with varying and unknown properties. A novel feature of the method is the use of a deep meta-learning framework to learn a prior energy model, which can efficiently adapt to the local terrain conditions based on small quantities of exteroceptive and proprioceptive data. A meta-adaptive heuristic function is also proposed for the integration of the energy model into an A* path planner. The performance of the proposed approach is assessed in a 3D-body dynamic simulator over several typologies of deformable terrains, and compared with alternative machine learning solutions. We provide evidence of the advantages of the proposed method to adapt to unforeseen terrain conditions, thereby yielding more informed estimations and energy-efficient paths, when navigating on unknown terrains.<div>Submitted for revision to IEEE Transaction on Cybernetics.</div>

scholarly journals A meta-learning approach for genomic survival analysis

2020 ◽  
Author(s):  
Yeping Lina Qiu ◽  
Hong Zheng ◽  
Arnout Devos ◽  
Olivier Gevaert
Keyword(s):  
Survival Analysis ◽  
Cancer Survival ◽  
Cancer Prognosis ◽  
Survival Prediction ◽  
Sequencing Data ◽  
Research Setting ◽  
Learning Framework ◽  
Meta Learning ◽  
Abundant Data ◽  
Common Situation

AbstractRNA sequencing has emerged as a promising approach in cancer prognosis as sequencing data becomes more easily and affordably accessible. However, it remains challenging to build good predictive models especially when the sample size is limited and the number of features is high, which is a common situation in biomedical settings. To address these limitations, we propose a meta-learning framework based on neural networks for survival analysis and evaluate it in a genomic cancer research setting. We demonstrate that, compared to regular transfer-learning, meta-learning is a significantly more effective paradigm to leverage high-dimensional data that is relevant but not directly related to the problem of interest. Specifically, meta-learning explicitly constructs a model, from abundant data of relevant tasks, to learn a new task with few samples effectively. For the application of predicting cancer survival outcome, we also show that the meta-learning framework with a few samples is able to achieve competitive performance with learning from scratch with a significantly larger number of samples. Finally, we demonstrate that the meta-learning model implicitly prioritizes genes based on their contribution to survival prediction and allows us to identify important pathways in cancer.

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