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Special Issue "Machine Learning with Applications: Dealing with Interpretability and Imbalanced Datasets"

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (30 July 2022) | Viewed by 10032

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Special Issue Editors

Prof. Dr. Maja Matetic

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Faculty of Informatics and Digital Technologies, University of Rijeka, 51000 Rijeka, Croatia
Interests: artificial intelligence; data science; machine learning; explainable artificial intelligence; explainable machine learning; human-centric AI; trustworthy Internet of Things systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Xiaoshuan Zhang

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Department of Mechatronics at the College of Engineering, Beijing Lab of Food Quality and Safety, China Agricultural University (East Campus), Beijing 100083, China
Interests: sensors (IoT, flexible sensors) and data processing in food supply chain/industrial engineering; live animal management
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Prof. Dr. Marija Brkić Bakarić

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Faculty of Informatics and Digital Technologies, University of Rijeka, Radmile Matejčić 2, 51000 Rijeka, Croatia
Interests: artificial intelligence; machine learning; interpretable machine learning; educational data mining; natural language processing; machine translation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A major disadvantage of using machine learning is that insights about the data are hidden in increasingly complex models. Moreover, the best performing models are often ensembles which cannot be interpreted, even if each single model could be interpreted. Explainable Machine Learning (Explanatory Artificial Intelligence, XAI) summarizes the reasons for black-box behaviour with the aim to gain the trust of users.

This Special Issue of Electronics will provide a forum for discussing exciting research on applying Interpretable Machine Learning (IML) methods on data captured by sensors or generated by interaction of users with systems in a variety of domains. Both original research articles and comprehensive review papers are welcome. We invite also submissions dealing with imbalanced classification problem in which the distribution of examples across the known classes is biased or skewed.

Topics of Interest of this Special Issue include, but are not limited to

Interpretability (intrinsic or post hoc)
- Global model interpretability
- Local model interpretability
Feature selection techniques
Imbalanced classification
Explainable AI decision support systems
Real-world applications of Interpretable Machine Learning in areas such as:
- Intelligent transportation systems
- Food safety
- Agriculture
- Natural Language Processing
- Education
- Healthcare
- Finance
- Smart cities

Prof. Dr. Maja Matetic
Prof. Dr. Xiaoshuan Zhang
Dr. Marija Brkić Bakarić
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Explainable machine learning
Interpretable machine learning
Model interpretability
Model-agnostic techniques
Trustworthy Internet of Things (IoT) systems
Rare event prediction
Extreme event prediction
Class imbalance
Feature selection

Published Papers (5 papers)

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Research

13 pages, 395 KiB

Open AccessArticle

KDE-Based Ensemble Learning for Imbalanced Data

Firuz Kamalov

Sherif Moussa

and

Jorge Avante Reyes

Electronics 2022, 11(17), 2703; https://doi.org/10.3390/electronics11172703 - 29 Aug 2022

Cited by 4 | Viewed by 1261

Abstract

Imbalanced class distribution affects many applications in machine learning, including medical diagnostics, text classification, intrusion detection and many others. In this paper, we propose a novel ensemble classification method designed to deal with imbalanced data. The proposed method trains each tree in the ensemble using uniquely generated synthetically balanced data. The data balancing is carried out via kernel density estimation, which offers a natural and effective approach to generating new sample points. We show that the proposed method results in a lower variance of the model estimator. The proposed method is tested against benchmark classifiers on a range of simulated and real-life data. The results of experiments show that the proposed classifier significantly outperforms the benchmark methods. Full article

(This article belongs to the Special Issue Machine Learning with Applications: Dealing with Interpretability and Imbalanced Datasets)

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16 pages, 3852 KiB

Open AccessArticle

Rapid and Accurate Diagnosis of COVID-19 Cases from Chest X-ray Images through an Optimized Features Extraction Approach

K. G. Satheesh Kumar

Arunachalam Venkatesan

Deepika Selvaraj

and

Alex Noel Joseph Raj

Electronics 2022, 11(17), 2682; https://doi.org/10.3390/electronics11172682 - 26 Aug 2022

Cited by 2 | Viewed by 1051

Abstract

The mutants of novel coronavirus (COVID-19 or SARS-Cov-2) are spreading with different variants across the globe, affecting human health and the economy. Rapid detection and providing timely treatment for the COVID-19 infected is the greater challenge. For fast and cost-effective detection, artificial intelligence (AI) can perform a key role in enhancing chest X-ray images and classifying them as infected/non-infected. However, AI needs huge datasets to train and detect the COVID-19 infection, which may impact the overall system speed. Therefore, Deep Neural Network (DNN) is preferred over standard AI models to speed up the classification with a set of features from the datasets. Further, to have accurate feature extraction, an algorithm that combines Zernike Moment Feature (ZMF) and Gray Level Co-occurrence Matrix Feature (GF) is proposed and implemented. The proposed algorithm uses 36 Zernike Moment features with variance and contrast textures. This helps to detect the COVID-19 infection accurately. Finally, the Region Blocking (RB) approach with an optimum sub-image size (32 × 32) is employed to improve the processing speed up to 2.6 times per image. The performance of this implementation presents an accuracy (A) of 93.4%, sensitivity (S_e) of 72.4%, specificity (S_p) of 95%, precision (P_r) of 74.9% and F1-score (F₁) of 72.3%. These metrics illustrate that the proposed model can identify the COVID-19 infection with a lesser dataset and improved accuracy up to 1.3 times than state-of-the-art existing models. Full article

(This article belongs to the Special Issue Machine Learning with Applications: Dealing with Interpretability and Imbalanced Datasets)

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17 pages, 5812 KiB

Open AccessArticle

The Impact of Partial Balance of Imbalanced Dataset on Classification Performance

and

Electronics 2022, 11(9), 1322; https://doi.org/10.3390/electronics11091322 - 21 Apr 2022

Cited by 2 | Viewed by 1412

Abstract

The imbalance of network data seriously affects the classification performance of algorithms. Most studies have only used a rough description of data imbalance with less exploration of the specific factors affecting classification performance, which has resulted in difficulty putting forward targeted solutions. In this paper, we find that the impact of medium categories on classification performance cannot be ignored, and therefore propose the concept of partial balance, consisting of Class Number of Partial Balance (β) and Balance Degree of Partial Samples (μ). Combined with Global Slope (α), a parameterized model is established to describe the difference of imbalanced datasets. Experiments are performed on the Moore Dataset and CICIDS 2017 Dataset. The experiment’s results on Random Forest, Decision Tree and Deep Neural Network show increasing α is a conducive step in the performance improvement of minority classes and overall classes. When β of dominant categories increases, that of inferior classes decreases, which results in a decrease in the average performance of minority classes. The lower μ is, the closer the sample size of medium classes is to the minority classes, and the better the average performance is. Based on the conclusions, we propose and verify some basic strategies by various classical algorithms. Full article

(This article belongs to the Special Issue Machine Learning with Applications: Dealing with Interpretability and Imbalanced Datasets)

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18 pages, 1934 KiB

Open AccessArticle

Utilization of Explainable Machine Learning Algorithms for Determination of Important Features in ‘Suncrest’ Peach Maturity Prediction

and

Electronics 2021, 10(24), 3115; https://doi.org/10.3390/electronics10243115 - 14 Dec 2021

Cited by 3 | Viewed by 2216

Abstract

Peaches (Prunus persica (L.) Batsch) are a popular fruit in Europe and Croatia. Maturity at harvest has a crucial influence on peach fruit quality, storage life, and consequently consumer acceptance. The main goal of this study is to develop a machine learning model that will detect the most important features for predicting peach maturity by first training models and then using the importance ratings of these models to detect nonlinear (and linear) relationships. Thus, the most important peach features at a given stage of its ripening could be revealed. To date, this method has not been used for this purpose, and at the same time, it has the potential to be applied to other similar peach varieties. A total of 33 fruit features are measured on the harvested peaches, and three imbalanced datasets are created using firmness thresholds of 1.84, 3.57, and 4.59 kg·cm⁻². These datasets are balanced using the SMOTE and ROSE techniques, and the Random Forest machine learning model is trained on them. Permutation Feature Importance (PFI), Variable Importance (VI), and LIME interpretability methods are used to detect variables that most influence predictions in the given machine learning models. PFI shows that the h° and a* ground color parameters, COL ground color index, SSC/TA, and TA inner quality parameters are among the top ten most contributing variables in all three models. Meanwhile, VI shows that this is the case for the a* ground color parameter, COL and CCL ground color indexes, and the SSC/TA inner quality parameter. The fruit flesh ratio is highly positioned (among the top three according to PFI) in two models, but it is not even among the top ten in the third. Full article

(This article belongs to the Special Issue Machine Learning with Applications: Dealing with Interpretability and Imbalanced Datasets)

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33 pages, 4326 KiB

Open AccessArticle

Estimation and Interpretation of Machine Learning Models with Customized Surrogate Model

and

Electronics 2021, 10(23), 3045; https://doi.org/10.3390/electronics10233045 - 06 Dec 2021

Cited by 2 | Viewed by 2496

Abstract

Machine learning has the potential to predict unseen data and thus improve the productivity and processes of daily life activities. Notwithstanding its adaptiveness, several sensitive applications based on such technology cannot compromise our trust in them; thus, highly accurate machine learning models require reason. Such models are black boxes for end-users. Therefore, the concept of interpretability plays the role if assisting users in a couple of ways. Interpretable models are models that possess the quality of explaining predictions. Different strategies have been proposed for the aforementioned concept but some of these require an excessive amount of effort, lack generalization, are not agnostic and are computationally expensive. Thus, in this work, we propose a strategy that can tackle the aforementioned issues. A surrogate model assisted us in building interpretable models. Moreover, it helped us achieve results with accuracy close to that of the black box model but with less processing time. Thus, the proposed technique is computationally cheaper than traditional methods. The significance of such a novel technique is that data science developers will not have to perform strenuous hands-on activities to undertake feature engineering tasks and end-users will have the graphical-based explanation of complex models in a comprehensive way—consequently building trust in a machine. Full article

(This article belongs to the Special Issue Machine Learning with Applications: Dealing with Interpretability and Imbalanced Datasets)

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