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Mathematical Optimization in Pattern Recognition, Machine Learning and Data Mining

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A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 11966

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Special Issue Editor

Dr. Valsamis Ntouskos

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Guest Editor

Remote Sensing Lab, School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, 15780 Athens, Greece
Interests: computer vision; deep learning; robot perception; convex optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue welcomes scientific manuscripts on two relevant topics for mathematical optimization in machine learning, pattern recognition, and data mining.

The adaptation of existing optimization techniques from mathematics or the development of novel approaches for addressing interesting problems in machine learning, pattern recognition, and data mining. This includes improvements to existing machine learning, deep learning, pattern recognition, and data mining algorithms. Improvements can include, but are not limited to, faster convergence rates, improved bounds, improved efficiency or memory consumption, better performance, etc. Improvements should be significant and demonstrated by corresponding empirical and theoretical evaluation.
The design of novel machine learning, pattern recognition, or data mining approaches to solve a specific domain task (this includes improving solutions for existing problems using, for example, heterogeneous or big data). The approach should not be a simple adaptation of existing algorithms to novel data. Proposed improvements must be supported by appropriate experiments and rigorous explanations of why existing approaches are not applicable.

Dr. Valsamis Ntouskos
Guest Editor

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. Mathematics 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 2600 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

mathematical optimization
machine learning
pattern recognition
data mining
convergence rate
theoretical bounds
time complexity
execution time
memory consumption
algorithm performance
domain-specific problems

Related Special Issue

Mathematical Optimization in Machine Learning, Computer Vision, and Data Mining, 2nd Edition in Mathematics

Published Papers (5 papers)

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Research

28 pages, 592 KiB

Open AccessArticle

Mathematical Modeling and Analysis of Credit Scoring Using the LIME Explainer: A Comprehensive Approach

by Abdussalam Aljadani, Bshair Alharthi, Mohammed A. Farsi, Hossam Magdy Balaha, Mahmoud Badawy and Mostafa A. Elhosseini

Mathematics 2023, 11(19), 4055; https://doi.org/10.3390/math11194055 - 25 Sep 2023

Cited by 3 | Viewed by 1801

Abstract

Credit scoring models serve as pivotal instruments for lenders and financial institutions, facilitating the assessment of creditworthiness. Traditional models, while instrumental, grapple with challenges related to efficiency and subjectivity. The advent of machine learning heralds a transformative era, offering data-driven solutions that transcend these limitations. This research delves into a comprehensive analysis of various machine learning algorithms, emphasizing their mathematical underpinnings and their applicability in credit score classification. A comprehensive evaluation is conducted on a range of algorithms, including logistic regression, decision trees, support vector machines, and neural networks, using publicly available credit datasets. Within the research, a unified mathematical framework is introduced, which encompasses preprocessing techniques and critical algorithms such as Particle Swarm Optimization (PSO), the Light Gradient Boosting Model, and Extreme Gradient Boosting (XGB), among others. The focal point of the investigation is the LIME (Local Interpretable Model-agnostic Explanations) explainer. This study offers a comprehensive mathematical model using the LIME explainer, shedding light on its pivotal role in elucidating the intricacies of complex machine learning models. This study’s empirical findings offer compelling evidence of the efficacy of these methodologies in credit scoring, with notable accuracies of 88.84%, 78.30%, and 77.80% for the Australian, German, and South German datasets, respectively. In summation, this research not only amplifies the significance of machine learning in credit scoring but also accentuates the importance of mathematical modeling and the LIME explainer, providing a roadmap for practitioners to navigate the evolving landscape of credit assessment. Full article

(This article belongs to the Special Issue Mathematical Optimization in Pattern Recognition, Machine Learning and Data Mining)

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19 pages, 18842 KiB

Open AccessArticle

Memory-Efficient Discrete Cosine Transform Domain Weight Modulation Transformer for Arbitrary-Scale Super-Resolution

by Min Hyuk Kim and Seok Bong Yoo

Mathematics 2023, 11(18), 3954; https://doi.org/10.3390/math11183954 - 18 Sep 2023

Cited by 1 | Viewed by 892

Abstract

Recently, several arbitrary-scale models have been proposed for single-image super-resolution. Furthermore, the importance of arbitrary-scale single image super-resolution is emphasized for applications such as satellite image processing, HR display, and video-based surveillance. However, the baseline integer-scale model must be retrained to fit the existing network, and the learning speed is slow. This paper proposes a network to solve these problems, processing super-resolution by restoring the high-frequency information lost in the remaining arbitrary-scale while maintaining the baseline integer scale. The proposed network extends an integer-scaled image to an arbitrary-scale target in the discrete cosine transform spectral domain. We also modulate the high-frequency restoration weights of the depthwise multi-head attention to use memory efficiently. Finally, we demonstrate the performance through experiments with existing state-of-the-art models and their flexibility through integration with existing integer-scale models in terms of peak signal-to-noise ratio (PSNR) and similarity index measure (SSIM) scores. This means that the proposed network restores high-resolution (HR) images appropriately by improving the image sharpness of low-resolution (LR) images. Full article

(This article belongs to the Special Issue Mathematical Optimization in Pattern Recognition, Machine Learning and Data Mining)

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Figure 1

17 pages, 5712 KiB

Open AccessArticle

ClueCatcher: Catching Domain-Wise Independent Clues for Deepfake Detection

by Eun-Gi Lee, Isack Lee and Seok-Bong Yoo

Mathematics 2023, 11(18), 3952; https://doi.org/10.3390/math11183952 - 17 Sep 2023

Cited by 1 | Viewed by 2108

Abstract

Deepfake detection is a focus of extensive research to combat the proliferation of manipulated media. Existing approaches suffer from limited generalizability and struggle to detect deepfakes created using unseen techniques. This paper proposes a novel deepfake detection method to improve generalizability. We observe domain-wise independent clues in deepfake images, including inconsistencies in facial colors, detectable artifacts at synthesis boundaries, and disparities in quality between facial and nonfacial regions. This approach uses an interpatch dissimilarity estimator and a multistream convolutional neural network to capture deepfake clues unique to each feature. By exploiting these clues, we enhance the effectiveness and generalizability of deepfake detection. The experimental results demonstrate the improved performance and robustness of this method. Full article

(This article belongs to the Special Issue Mathematical Optimization in Pattern Recognition, Machine Learning and Data Mining)

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Figure 1

26 pages, 2226 KiB

Open AccessArticle

A Mathematical Model for Customer Segmentation Leveraging Deep Learning, Explainable AI, and RFM Analysis in Targeted Marketing

by Fatma M. Talaat, Abdussalam Aljadani, Bshair Alharthi, Mohammed A. Farsi, Mahmoud Badawy and Mostafa Elhosseini

Mathematics 2023, 11(18), 3930; https://doi.org/10.3390/math11183930 - 15 Sep 2023

Cited by 2 | Viewed by 4584

Abstract

In the evolving landscape of targeted marketing, integrating deep learning (DL) and explainable AI (XAI) offers a promising avenue for enhanced customer segmentation. This paper introduces a groundbreaking approach, DeepLimeSeg, which synergizes DL methodologies with Lime-based Explainability to segment customers effectively. The approach employs a comprehensive mathematical model to harness demographic data, behavioral patterns, and purchase histories, categorizing customers into distinct clusters aligned with their preferences and needs. A pivotal component of this research is the mathematical underpinning of the DeepLimeSeg approach. The Lime-based Explainability module ensures that the segmentation results are accurate and interpretable. The mathematical rigor facilitates businesses tailoring their marketing strategies with precision, optimizing sales outcomes. To validate the efficacy of DeepLimeSeg, we employed two real-world datasets: Mall-Customer Segmentation Data and an E-Commerce dataset. A comparative analysis between DeepLimeSeg and the traditional Recency, Frequency, and Monetary (RFM) analysis is presented. The RFM analysis, grounded in its mathematical modeling, segments customers based on purchase recency, frequency, and monetary value. Our preprocessing involved computing RFM scores for each customer, followed by K-means clustering to delineate customer segments. Empirical results underscored the superiority of DeepLimeSeg over other models in terms of MSE, MAE, and

R^{2}

metrics. Specifically, the model registered an MSE of 0.9412, indicative of its robust predictive accuracy concerning the spending score. The MAE value stood at 0.9874, signifying minimal deviation from actual values. This paper accentuates the importance of mathematical modeling in enhancing customer segmentation. The DeepLimeSeg approach, with its mathematical foundation and explainable AI integration, paves the way for businesses to make informed, data-driven marketing decisions. Full article

(This article belongs to the Special Issue Mathematical Optimization in Pattern Recognition, Machine Learning and Data Mining)

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25 pages, 1492 KiB

Open AccessArticle

Semi-Supervised Multi-Label Dimensionality Reduction Learning by Instance and Label Correlations

by Runxin Li, Jiaxing Du, Jiaman Ding, Lianyin Jia, Yinong Chen and Zhenhong Shang

Mathematics 2023, 11(3), 782; https://doi.org/10.3390/math11030782 - 3 Feb 2023

Cited by 2 | Viewed by 1675

Abstract

The label learning mechanism is challenging to integrate into the training model of the multi-label feature space dimensionality reduction problem, making the current multi-label dimensionality reduction methods primarily supervision modes. Many methods only focus attention on label correlations and ignore the instance interrelations between the original feature space and low dimensional space. Additionally, very few techniques consider how to constrain the projection matrix to identify specific and common features in the feature space. In this paper, we propose a new approach of semi-supervised multi-label dimensionality reduction learning by instance and label correlations (SMDR-IC, in short). Firstly, we reformulate MDDM which incorporates label correlations as a least-squares problem so that the label propagation mechanism can be effectively embedded into the model. Secondly, we investigate instance correlations using the k-nearest neighbor technique, and then present the

l_{1}

-norm and

l_{2, 1}

-norm regularization terms to identify the specific and common features of the feature space. Experiments on the massive public multi-label data sets show that SMDR-IC has better performance than other related multi-label dimensionality reduction methods. Full article

(This article belongs to the Special Issue Mathematical Optimization in Pattern Recognition, Machine Learning and Data Mining)

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