Challenges In Machine Learning Research Articles

Regression-Based Hyperparameter Learning for Support Vector Machines.

Unification of classification and regression is a major challenge in machine learning and has attracted increasing attentions from researchers. In this article, we present a new idea for this challenge, where we convert the classification problem into a regression problem, and then use the methods in regression to solve the problem in classification. To this end, we leverage the widely used maximum margin classification algorithm and its typical representative, support vector machine (SVM). More specifically, we convert SVM into a piecewise linear regression task and propose a regression-based SVM (RBSVM) hyperparameter learning algorithm, where regression methods are used to solve several key problems in classification, such as learning of hyperparameters, calculation of prediction probabilities, and measurement of model uncertainty. To analyze the uncertainty of the model, we propose a new concept of model entropy, where the leave-one-out prediction probability of each sample is converted into entropy, and then used to quantify the uncertainty of the model. The model entropy is different from the classification margin, in the sense that it considers the distribution of all samples, not just the support vectors. Therefore, it can assess the uncertainty of the model more accurately than the classification margin. In the case of the same classification margin, the farther the sample distribution is from the classification hyperplane, the lower the model entropy. Experiments show that our algorithm (RBSVM) provides higher prediction accuracy and lower model uncertainty, when compared with state-of-the-art algorithms, such as Bayesian hyperparameter search and gradient-based hyperparameter learning algorithms.

Exploratory Analysis of Methods, Techniques, and Metrics to Handle Class Imbalance Problem

Class imbalance a common challenge in machine learning, often results in skewed predictions and misrepresentative model assessments, highlighting the need for effective countermeasures. Our detailed survey dives into three primary techniques for addressing this issue: data-level interventions, algorithmic modifications, and integrated hybrid solutions. We thoroughly dissect each approach, delineating its merits, drawbacks, and ideal use cases. Data-level methods aim to restructure the dataset for class balance, while algorithmic techniques recalibrate the learning process to better detect the minority class. The hybrid strategies merge the benefits of both for a holistic remedy. The study further emphasizes the importance of precise evaluation metrics, elaborating on both conventional metrics and those tailored for imbalanced scenarios. Our objective is to arm professionals with a deep insight into tackling class imbalance, especially within the big data framework. The insights shared aspire to inspire the creation of resilient, equitable machine learning models adapt at navigating imbalanced data, ensuring enhanced prediction fidelity and consistency.

Learning Generative Models Using Denoising Density Estimators.

Learning probabilistic models that can estimate the density of a given set of samples, and generate samples from that density, is one of the fundamental challenges in unsupervised machine learning. We introduce a new generative model based on denoising density estimators (DDEs), which are scalar functions parametrized by neural networks, that are efficiently trained to represent kernel density estimators of the data. Leveraging DDEs, our main contribution is a novel technique to obtain generative models by minimizing the Kullback-Leibler (KL)-divergence directly. We prove that our algorithm for obtaining generative models is guaranteed to converge consistently to the correct solution. Our approach does not require specific network architecture as in normalizing flows (NFs), nor use ordinary differential equation (ODE) solvers as in continuous NFs. Experimental results demonstrate substantial improvement in density estimation and competitive performance in generative model training.

Systematic Analysis of threats, Machine Learning solutions and Challenges for Securing IoT environment

The Internet of Things (IoT) has revolutionized our daily lives, impacting everything from healthcare to transportation and even home automation and industrial control systems. However, as the number of connected devices continues to rise, so do the security risks. In this review, we explore the different types of attacks that target various layers of IoT infrastructure. To counter these threats, researchers have proposed using machine learning (ML) and deep learning (DL) techniques for detecting different types of attacks. However, our examination of existing literature reveals that the effectiveness of these techniques can vary greatly depending on factors like the dataset used, the features considered, and the evaluation methods employed. Finally, we delve into the current challenges facing Intrusion Detection Systems (IDS) in their mission to protect IoT environments from evolving threats.

Towards efficiently solving the rubik’s cube with deep reinforcement learning and recursion

The Rubik’s cube is a prototypical combinatorial puzzle that has a large state space with a single goal state. The goal state is unlikely to be retrieved using orders of randomly generated moves, posing unique challenges for machine learning. The proposed work is above to solve the Rubik’s cube with recursion and DeepCubeA, a deep reinforcement learning approach that learns how to solve increasingly difficult states in reverse from the goal state without any specific domain knowledge. DeepCubeA solves 100% of all test patterns, finding a shortest path to the goal state 60.3% of the time. Deep Cube A generalizes to other combinatorial puzzles andis able to solve the 15 puzzle, 24 puzzle, 35 puzzle, 48 puzzle, Lights Out and Sokoban, finding a shortest path in the majority of verifiable cases. These models were trained with 1 4 GPUs and 20 30 CPUs. This varies throughout training as the training is often stopped and started again to make room for other processes. Further our experimentation compares the results of Rubik’s cube solving among both recursion and DeepCubeA and also with the state of art models. Later, we intend to develop a new deep learning model with an application.

Biarchetype Analysis: Simultaneous Learning of Observations and Features Based on Extremes.

We introduce a novel exploratory technique, termed biarchetype analysis, which extends archetype analysis to simultaneously identify archetypes of both observations and features. This innovative unsupervised machine learning tool aims to represent observations and features through instances of pure types, or biarchetypes, which are easily interpretable as they embody mixtures of observations and features. Furthermore, the observations and features are expressed as mixtures of the biarchetypes, which makes the structure of the data easier to understand. We propose an algorithm to solve biarchetype analysis. Although clustering is not the primary aim of this technique, biarchetype analysis is demonstrated to offer significant advantages over biclustering methods, particularly in terms of interpretability. This is attributed to biarchetypes being extreme instances, in contrast to the centroids produced by biclustering, which inherently enhances human comprehension. The application of biarchetype analysis across various machine learning challenges underscores its value, and both the source code and examples are readily accessible in R and Python at https://github.com/aleixalcacer/JA-BIAA.

Opportunities and Challenges of Remote Sensing, Geospatial Data, and Machine Learning in Obtaining Accessibility and Location Information for Sustainable Development in Indonesia

With the advancement of technologies so does the data collection method which creates a large, rapid, and diverse stream of data. Statistic Indonesia (BPS) has also encouraged to utilize this by starting to collect geospatial information on respondents and public facilities. To keep up with this a change needs to be made in processing methods to accommodate massive, high-dimensional, and multiform data collected in different forms such as machine learning. This progression also opens up a new opportunity for tackling various statistical data problems such as accessibility and location data. Remote sensing is one of the big data sources that undergoes a lot of changes shown in the high spatial and temporal resolution satellite imagery availability, together with the BPS geotagging data shows great promise in classifying land use and geospatial analysis. Even so, there are still some challenges in remote sensing as well as other geospatial data utilization. The goals of this review paper are to study the opportunities and challenges in utilizing remote sensing, geospatial data, and machine learning for accessibility and location information. In this paper, we explore the possibilities and limitations in its implementation into SDGs indicators that involve accessibility and location such as indicators 9.1.1, 11.1.1, 11.2.1, 11.3.1, and 11.7.1 including other variables needed for the calculation like access to public facilities. Moreover, our experiment using geotagging data shows potential in improving proportion estimation when compared to using a simple ratio. Our DEGURBA following the UN definition using machine learning LULC for dasymetric mapping also provides more insight compared to the existing data. We can conclude that there are great opportunities in applying remote sensing and other geospatial data to monitor the accessibility and location to further sustainable development in Indonesia.

Implementasi Fungsi Polinomial pada Algoritma Gradient Boosting Regressor: Studi Regresi pada Dataset Obat-Obatan Kadaluarsa Sebagai Material Antikorosi

Corrosion is an electrochemical process between the metal surface and a corrosive environment that can lead to significant losses in various industries, especially in the oil and gas sector. Experimental studies are conducted to evaluate the performance of corrosion inhibitors and available resources. In this research, a machine learning (ML) approach is employed to assess the effectiveness of expired drug compounds as corrosion inhibitors. The primary challenge in machine learning is obtaining a highly accurate model to ensure that predictions are relevant to the properties of the tested materials. Therefore, the polynomial function is tested in the gradient-boosting regressor (GBR) algorithm to enhance the accuracy of the developed ML model. The results indicate that the implementation of the polynomial function in the GBR algorithm can improve the accuracy of the prediction model based on R2 and RMSE metrics.

The Challenges of Machine Learning in Software Development

A documentary review was carried out on the production and publication of research papers related to the study of the variables Machine Learning and Software Development. The purpose of the bibliometric analysis proposed in this document was to know the main characteristics of the volume of publications registered in the Scopus database during the period 2017-2022 by Latin American institutions, achieving the identification of 307 publications. The information provided by this platform was organized through graphs and figures, categorizing the information by Year of Publication, Country of Origin, Area of Knowledge and Type of Publication. Once these characteristics have been described, the position of different authors on the proposed topic is referenced through a qualitative analysis. Among the main findings made through this research, it is found that Brazil, with 153 publications, was the Latin American country with the highest scientific production registered in the name of authors affiliated with institutions of that nation. The Area of Knowledge that made the greatest contribution to the construction of bibliographic material related to the study of Machine Learning and Software Development was Computer Science with 256 published documents, and the most used Publication Type during the period indicated above were Conference Articles with 52% of the total scientific production.

Semi-White-Box Strategy: Enhancing Data Efficiency and Interpretability of Convolutional Neural Networks in Image Processing

Data-hunger is a persistent challenge in machine learning, particularly in the field of image processing based on convolutional neural networks (CNNs). This study systematically investigates the factors contributing to data-hunger in machine-learning-based image-processing algorithms. The results revealed that the proliferation of model parameters, the lack of interpretability, and the complexity of model structure are significant factors influencing data-hunger. Based on these findings, this paper introduces a novel semi-white-box neural network model construction strategy. This approach effectively reduces the number of model parameters while enhancing the interpretability of model components. It accomplishes this by constraining uninterpretable processes within the model and leveraging prior knowledge of image processing for model. Rather than relying on a single all-in-one model, a semi-white-box model is composed of multiple smaller models, each responsible for extracting fundamental semantic features. The final output is derived from these features and prior knowledge. The proposed strategy holds the potential to substantially decrease data requirements under specific data source conditions while improving the interpretability of model components. Validation experiments are conducted on well-established datasets, including MNIST, Fashion MNIST, CIFAR, and generated data. The results demonstrate the superiority of the semi-white-box strategy over the traditional all-in-one approach in terms of accuracy when trained with equivalent data volumes. Impressively, on the tested datasets, a simplified semi-white-box model achieves performance close to that of ResNet while utilizing a small number of parameters. Furthermore, the semi-white-box strategy offers improved interpretability and parameter reusability features that are challenging to achieve with the all-in-one approach. In conclusion, this paper contributes to mitigating data-hunger challenges in machine-learning-based image processing through the introduction of a novel semi-white-box model construction strategy, backed by empirical evidence of its effectiveness.

Fitting a collider in a quantum computer: tackling the challenges of quantum machine learning for big datasets.

Current quantum systems have significant limitations affecting the processing of large datasets with high dimensionality, typical of high energy physics. In the present paper, feature and data prototype selection techniques were studied to tackle this challenge. A grid search was performed and quantum machine learning models were trained and benchmarked against classical shallow machine learning methods, trained both in the reduced and the complete datasets. The performance of the quantum algorithms was found to be comparable to the classical ones, even when using large datasets. Sequential Backward Selection and Principal Component Analysis techniques were used for feature's selection and while the former can produce the better quantum machine learning models in specific cases, it is more unstable. Additionally, we show that such variability in the results is caused by the use of discrete variables, highlighting the suitability of Principal Component analysis transformed data for quantum machine learning applications in the high energy physics context.

Recent trends and advances in machine learning challenges and applications for industry 4.0

AbstractThis editorial summarizes and analyzes 17 articles selected for a special issue on machine learning advances for Industry 4.0 applications. The diverse articles cover fault detection, deep learning optimisation, IoT networking, vehicle control, recommendation systems and domain knowledge integration. Key methods represented include neural networks, deep learning, reinforcement learning and explainable AI. Real‐world industrial case studies showcase machine learning's versatility in enabling intelligent automation, control, and decision‐making across manufacturing, healthcare, transportation and other sectors. While highlighting theoretical innovations, the contributions also demonstrate machine learning's transformative potential for intelligent, connected, self‐optimising next generation production systems. This editorial concisely overviews the latest trends represented in this special issue.

WRND: A weighted oversampling framework with relative neighborhood density for imbalanced noisy classification

Imbalanced data and label noise are ubiquitous challenges in data mining and machine learning that severely impair classification performance. The synthetic minority oversampling technique (SMOTE) and its variants have been proposed, but they are easily constrained by hyperparameter optimization such as k-nearest neighbors, their performance deteriorates owing to noise, they rarely consider data distribution information, and they cause high complexity. Furthermore, SMOTE-based methods perform random linear interpolation between each minority class sample and its randomly selected k-nearest neighbors, regardless of sample differences and distribution information. To address the above problems, an adaptive, robust, and general weighted oversampling framework based on relative neighborhood density (WRND) is proposed. It can combine with most SMOTE-based sampling algorithms easily and improve performance. First, it adaptively distinguishes and filters noisy and outlier samples by introducing the natural neighbor, which inherently avoids the extra noise and overlapping samples introduced by the synthesis of noisy samples. The relative neighborhood density of each sample can then be obtained, which reflects the intra-class and inter-class distribution information within the natural neighborhood. To alleviate the blindness of SMOTE-based methods, the number and locations of synthetic samples are informedly assigned based on distribution information and reasonable generalization of natural neighborhoods of original samples. Extensive experiments on 23 benchmark datasets and six classic classifiers with eight pairs of representative sampling algorithms and two state-of-the-art frameworks, significantly demonstrate the effectiveness of the WRND framework. Code and framework are available at https://github.com/dream-lm/WRND_framework.

MicroBiomeGSM: the identification of taxonomic biomarkers from metagenomic data using grouping, scoring and modeling (G-S-M) approach

Numerous biological environments have been characterized with the advent of metagenomic sequencing using next generation sequencing which lays out the relative abundance values of microbial taxa. Modeling the human microbiome using machine learning models has the potential to identify microbial biomarkers and aid in the diagnosis of a variety of diseases such as inflammatory bowel disease, diabetes, colorectal cancer, and many others. The goal of this study is to develop an effective classification model for the analysis of metagenomic datasets associated with different diseases. In this way, we aim to identify taxonomic biomarkers associated with these diseases and facilitate disease diagnosis. The microBiomeGSM tool presented in this work incorporates the pre-existing taxonomy information into a machine learning approach and challenges to solve the classification problem in metagenomics disease-associated datasets. Based on the G-S-M (Grouping-Scoring-Modeling) approach, species level information is used as features and classified by relating their taxonomic features at different levels, including genus, family, and order. Using four different disease associated metagenomics datasets, the performance of microBiomeGSM is comparatively evaluated with other feature selection methods such as Fast Correlation Based Filter (FCBF), Select K Best (SKB), Extreme Gradient Boosting (XGB), Conditional Mutual Information Maximization (CMIM), Maximum Likelihood and Minimum Redundancy (MRMR) and Information Gain (IG), also with other classifiers such as AdaBoost, Decision Tree, LogitBoost and Random Forest. microBiomeGSM achieved the highest results with an Area under the curve (AUC) value of 0.98% at the order taxonomic level for IBDMD dataset. Another significant output of microBiomeGSM is the list of taxonomic groups that are identified as important for the disease under study and the names of the species within these groups. The association between the detected species and the disease under investigation is confirmed by previous studies in the literature. The microBiomeGSM tool and other supplementary files are publicly available at: https://github.com/malikyousef/microBiomeGSM.

Causal Factor Disentanglement for Few-Shot Domain Adaptation in Video Prediction.

An important challenge in machine learning is performing with accuracy when few training samples are available from the target distribution. If a large number of training samples from a related distribution are available, transfer learning can be used to improve the performance. This paper investigates how to do transfer learning more effectively if the source and target distributions are related through a Sparse Mechanism Shift for the application of next-frame prediction. We create Sparse Mechanism Shift-TempoRal Intervened Sequences (SMS-TRIS), a benchmark to evaluate transfer learning for next-frame prediction derived from the TRIS datasets. We then propose to exploit the Sparse Mechanism Shift property of the distribution shift by disentangling the model parameters with regard to the true causal mechanisms underlying the data. We use the Causal Identifiability from TempoRal Intervened Sequences (CITRIS) model to achieve this disentanglement via causal representation learning. We show that encouraging disentanglement with the CITRIS extensions can improve performance, but their effectiveness varies depending on the dataset and backbone used. We find that it is effective only when encouraging disentanglement actually succeeds in increasing disentanglement. We also show that an alternative method designed for domain adaptation does not help, indicating the challenging nature of the SMS-TRIS benchmark.

Con2Mix: A semi-supervised method for imbalanced tabular security data1

Con2Mix (Contrastive Double Mixup) is a new semi-supervised learning methodology that innovates a triplet mixup data augmentation approach for finding code vulnerabilities in imbalanced, tabular security data sets. Tabular data sets in cybersecurity domains are widely known to pose challenges for machine learning because of their heavily imbalanced data (e.g., a small number of labeled attack samples buried in a sea of mostly benign, unlabeled data). Semi-supervised learning leverages a small subset of labeled data and a large subset of unlabeled data to train a learning model. While semi-supervised methods have been well studied in image and language domains, in security domains they remain underutilized, especially on tabular security data sets which pose especially difficult contextual information loss and balance challenges for machine learning. Experiments applying Con2Mix to collected security data sets show promise for addressing these challenges, achieving state-of-the-art performance on two evaluated data sets compared with other methods.

An Overview of Hyperspectral Image Classification by Data-driven Deep Learning

Hyperspectral imaging (HSI) in remote sensing is gaining significant attention due to its complexity, posing challenges for conventional machine learning in achieving accurate classification. The inherent nonlinear relationship between captured spectral information and materials further complicates hyperspectral imaging. Deep learning has emerged as an effective tool for feature extraction, finding widespread applications in image processing tasks. Motivated by its success, this survey integrates deep learning into hyperspectral imaging (HSI) classification, demonstrating commendable performance. The paper systematically reviews existing literature, providing a comparative analysis of strategies. Primary challenges in HSI classification for traditional methods are outlined, emphasizing the advantages of deep learning. Our framework categorizes works into three types: spectral-feature networks, spatial-feature networks, and spectral-spatial-feature networks, offering a comprehensive review of recent achievements and diverse approaches. Considering limited training samples in remote sensing and substantial data requirements for deep networks, strategies to enhance classification performance are presented, offering valuable insights for future studies. Experiments apply representative deep learning-based classification methods to real HSIs, providing practical validation. The survey contributes to understanding the current landscape in deep learning-based HSI classification and lays a foundation for future research in this evolving field.

Bring memristive in-memory computing into general-purpose machine learning: A perspective

In-memory computing (IMC) using emerging nonvolatile devices has received considerable attention due to its great potential for accelerating artificial neural networks and machine learning tasks. As the basic concept and operation modes of IMC are now well established, there is growing interest in employing its wide and general application. In this perspective, the path that leads memristive IMC to general-purpose machine learning is discussed in detail. First, we reviewed the development timeline of machine learning algorithms that employ memristive devices, such as resistive random-access memory and phase-change memory. Then we summarized two typical aspects of realizing IMC-based general-purpose machine learning. One involves a heterogeneous computing system for algorithmic completeness. The other is to obtain the configurable precision techniques for the compromise of the precision-efficiency dilemma. Finally, the major directions and challenges of memristive IMC-based general-purpose machine learning are proposed from a cross-level design perspective.

XGBoost and Random Forest Algorithms: An in Depth Analysis

Machine learning is playing an increasingly important role in many facets of our lives as technology develops, including forecasting weather, figuring out social media trends, and predicting prices on the world market. This significance invoked the demand of some efficient predicting models that can easily handle complex data and provide maximum accurate results. XGBoost and Random Forest are upgradable ensemble techniques used to solve regression and classification problems that have evolved and proved to be dependable and reliable machine learning challenge solvers. In this research paper, we undertake a comprehensive analysis and comparison of these two prominent machine learning algorithms. The first half of the research includes relevant overview on both of the techniques, significance and evolution of both algorithms. The latter part of this study involves a meticulous comparative analysis between Random Forest and XGBoost, scrutinizing facets such as time complexity, precision, and reliability. We examine their distinctive approaches to handling regression and classification problems while closely examining their subtle handling of training and testing datasets. A thorough quantitative evaluation using a variety of performance metrics, such as the F1-score, Recall, Precision, Mean Squared Erro

Application and Challenges of Machine Learning in Healthcare

Abstract: The integration of machine learning (ML) techniques into healthcare has emerged as a transformative force, revolutionizing various aspects of patient care, disease management, and healthcare operations. This research paper explores the manifold applications and accompanying challenges associated with the utilization of ML in healthcare. Machine learning finds extensive application in healthcare, encompassing early disease detection, personalized treatment plans, drug discovery, medical image analysis, and patient risk stratification. It plays a pivotal role in clinical decision support, enhancing diagnostic accuracy and treatment effectiveness. Furthermore, ML-based telemedicine and remote monitoring solutions have expanded healthcare accessibility, particularly in remote or underserved areas. Despite its remarkable potential, the adoption of ML in healthcare is not without challenges. Data privacy and security concerns are paramount, as sensitive patient information is processed. Data quality, interoperability issues, and ethical considerations related to algorithm bias and transparency demand vigilant attention. Regulatory hurdles and resistance to change among healthcare professionals add complexity to the integration process. Ethical considerations emerge prominently as healthcare providers increasingly rely on ML-driven insights. This paper discusses the ethical dimensions surrounding patient data privacy, informed consent, and the need for transparent and unbiased algorithms. Looking ahead, the research identifies future trends and opportunities in the intersection of ML and healthcare. As technology evolves, AI ethics and responsible AI principles will play a pivotal role in shaping the ethical framework of healthcare. Real-world case studies underscore the significant impact of ML in healthcare and provide valuable insights into success factors and challenges faced in various healthcare contexts. In conclusion, machine learning holds great promise in revolutionizing healthcare, but its implementation necessitates addressing complex challenges, especially ethical concerns. This paper serves as a comprehensive overview of the state of ML in healthcare, offering recommendations for stakeholders and a vision for an ethically-driven, technology-empowered future in healthcare