mCRF and mRD: Two Classification Methods Based on a Novel Multiclass Label Noise Filtering Learning Framework

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.

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Frequency-Temporal Disagreement Adaptation for Robotic Terrain Classification via Vibration in a Dynamic Environment

Sensors ◽

10.3390/s20226550 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6550 ◽

Cited By ~ 1

Author(s):

Chen Cheng ◽

Ji Chang ◽

Wenjun Lv ◽

Yuping Wu ◽

Kun Li ◽

...

Keyword(s):

Incremental Learning ◽

Classification Accuracy ◽

Concept Drift ◽

Dynamic Environment ◽

Temporal Correlation ◽

Classification Methods ◽

Terrain Classification ◽

Localization Accuracy ◽

Learning Framework ◽

Control Scheme

The accurate terrain classification in real time is of great importance to an autonomous robot working in field, because the robot could avoid non-geometric hazards, adjust control scheme, or improve localization accuracy, with the aid of terrain classification. In this paper, we investigate the vibration-based terrain classification (VTC) in a dynamic environment, and propose a novel learning framework, named DyVTC, which tackles online-collected unlabeled data with concept drift. In the DyVTC framework, the exterior disagreement (ex-disagreement) and interior disagreement (in-disagreement) are proposed novely based on the feature diversity and intrinsic temporal correlation, respectively. Such a disagreement mechanism is utilized to design a pseudo-labeling algorithm, which shows its compelling advantages in extracting key samples and labeling; and consequently, the classification accuracy could be retrieved by incremental learning in a changing environment. Since two sets of features are extracted from frequency and time domain to generate disagreements, we also name the proposed method feature-temporal disagreement adaptation (FTDA). The real-world experiment shows that the proposed DyVTC could reach an accuracy of 89.5%, but the traditional time- and frequency-domain terrain classification methods could only reach 48.8% and 71.5%, respectively, in a dynamic environment.

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Improving Label Noise Filtering by Exploiting Unlabeled Data

IEEE Access ◽

10.1109/access.2018.2807779 ◽

2018 ◽

Vol 6 ◽

pp. 11154-11165 ◽

Cited By ~ 1

Author(s):

Donghai Guan ◽

Hongqiang Wei ◽

Weiwei Yuan ◽

Guangjie Han ◽

Yuan Tian ◽

...

Keyword(s):

Unlabeled Data ◽

Noise Filtering ◽

Label Noise

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Noise Removal Process from Label Classification using Machine Learning

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c3920.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 172-175

Keyword(s):

Machine Learning ◽

Big Data ◽

Supervised Learning ◽

Noise Removal ◽

Error Rates ◽

Training Data ◽

Learning Performance ◽

Training Dataset ◽

Noise Filtering ◽

Label Noise

Text classification and clustering approach is essential for big data environments. In supervised learning applications many classification algorithms have been proposed. In the era of big data, a large volume of training data is available in many machine learning works. However, there is a possibility of mislabeled or unlabeled data that are not labeled properly. Some labels may be incorrect resulted in label noise which in turn regress learning performance of a classifier. A general approach to address label noise is to apply noise filtering techniques to identify and remove noise before learning. A range of noise filtering approaches have been developed to improve the classifiers performance. This paper proposes noise filtering approach in text data during the training phase. Many supervised learning algorithms generates high error rates due to noise in training dataset, our work eliminates such noise and provides accurate classification system.

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A Comparative Study of Classification Methods Using Data with Label Noise

The Korean Data Analysis Society ◽

10.37727/jkdas.2018.20.6.2853 ◽

2018 ◽

Vol 20 (6) ◽

pp. 2853-2864

Author(s):

So Young Kwon ◽

Kyoung Hee Kim

Keyword(s):

Comparative Study ◽

Classification Methods ◽

Label Noise ◽

Using Data

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Cluster Validation Measures for Label Noise Filtering

2018 International Conference on Intelligent Systems (IS) ◽

10.1109/is.2018.8710495 ◽

2018 ◽

Cited By ~ 2

Author(s):

Veselka Boeva ◽

Lars Lundberg ◽

Milena Angelova ◽

Jan Kohstall

Keyword(s):

Noise Filtering ◽

Cluster Validation ◽

Label Noise ◽

Cluster Validation Measures

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Capped ℓ1-norm regularized least squares classification with label noise

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-200432 ◽

2021 ◽

pp. 1-13

Author(s):

Zhi Yang ◽

Haitao Gan ◽

Xuan Li ◽

Cong Wu

Keyword(s):

Machine Learning ◽

Least Squares ◽

Supervised Learning ◽

State Of The Art ◽

Negative Influence ◽

Optimization Approach ◽

Classification Methods ◽

Label Noise ◽

Negative Effect ◽

Novel Method

Since label noise can hurt the performance of supervised learning (SL), how to train a good classifier to deal with label noise is an emerging and meaningful topic in machine learning field. Although many related methods have been proposed and achieved promising performance, they have the following drawbacks: (1) they can lead to data waste and even performance degradation if the mislabeled instances are removed; and (2) the negative effect of the extremely mislabeled instances cannot be completely eliminated. To address these problems, we propose a novel method based on the capped ℓ1 norm and a graph-based regularizer to deal with label noise. In the proposed algorithm, we utilize the capped ℓ1 norm instead of the ℓ1 norm. The used norm can inherit the advantage of the ℓ1 norm, which is robust to label noise to some extent. Moreover, the capped ℓ1 norm can adaptively find extremely mislabeled instances and eliminate the corresponding negative influence. Additionally, the proposed algorithm makes full use of the mislabeled instances under the graph-based framework. It can avoid wasting collected instance information. The solution of our algorithm can be achieved through an iterative optimization approach. We report the experimental results on several UCI datasets that include both binary and multi-class problems. The results verified the effectiveness of the proposed algorithm in comparison to existing state-of-the-art classification methods.

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