Current Machine Learning Algorithms Research Articles

Feature-Free Explainable Data Mining in SAR Images Using Latent Dirichlet Allocation

In this article, we propose a promising approach for the application-oriented content classification of spaceborne radar imagery that presents an interesting alternative to popular current machine learning algorithms. In the following, we consider the problem of unsupervised feature-free satellite image classification with already known classes as an explainable data mining problem for regions with no prior information. Three important issues are addressed here: explainability, feature independence, and unsupervision. There is an increasing demand toward explainable machine learning models as they strive to meet the “right to explanation.” The importance of feature-free classification stems from the problem that different classification outcomes are obtained from using different features and the complexity of computing sophisticated image primitive features. Developing unsupervised discovery techniques helps overcome the limitations in object discovery due to the lack of labeled data and the dependence on features. In this article, we demonstrate the applicability of a latent Dirichlet allocation (LDA) model, one of the most established unsupervised probabilistic methods, in discovering the latent structure of synthetic aperture radar data. The idea is to use LDA as an explainable data mining tool to discover scientifically explainable semantic relations. The suitability of the approach as an explainable model is discussed and interpretable topic representation maps are produced, which practically demonstrate the idea of “interpretability” in an explainable machine learning paradigm. LDA discovers the latent structures in the data as a set of topics. We create the interpretable visualizations of the data utilizing these topics and compute the topic distributions for each land-cover class. Our results show that each class has a distinct topic distribution that represents that particular class. Then these classes can be grouped based on their similarity of topic composition. Both the topic composition and grouping are explainable by domain experts.

Big Data Analytics in Healthcare using Machine Learning Algorithms: A Comparative Study

In recent years vast quantities of data have been managed in various ways of medical applications and multiple organizations worldwide have developed this type of data and, together, these heterogeneous data are called big data. Data with other characteristics, quantity, speed and variety are the word big data. The healthcare sector has faced the need to handle the large data from different sources, renowned for generating large amounts of heterogeneous data. We can use the Big Data analysis to make proper decision in the health system by tweaking some of the current machine learning algorithms. If we have a large amount of knowledge that we want to predict or identify patterns, master learning would be the way forward. In this article, a brief overview of the Big Data, functionality and ways of Big data analytics are presented, which play an important role and affect healthcare information technology significantly. Within this paper we have presented a comparative study of algorithms for machine learning. We need to make effective use of all the current machine learning algorithms to anticipate accurate outcomes in the world of nursing.

The balancing trick: Optimized sampling of imbalanced datasets—A brief survey of the recent State of the Art

AbstractThis survey paper focuses on one of the current primary issues challenging data mining researchers experimenting on real‐world datasets. The problem is that of imbalanced class distribution that generates a bias toward the majority class due to insufficient training samples from the minority class. The current machine learning and deep learning algorithms are trained on datasets that are insufficiently represented in certain categories. On the other hand, some other classes have surplus samples due to the ready availability of data from these categories. Conventional solutions suggest undersampling of the majority class and/or oversampling of the minority class for balancing the class distribution prior to the learning phase. Though this problem of uneven class distribution is, by and large, ignored by researchers focusing on the learning technology, a need has now arisen for incorporating balance correction and data pruning procedures within the learning process itself. This paper surveys a plethora of conventional and recent techniques that address this issue through intelligent representations of samples from the majority and minority classes, that are given as input to the learning module. The application of nature‐inspired evolutionary algorithms to intelligent sampling is examined, and so are hybrid sampling strategies that select and retain the difficult‐to‐learn samples and discard the easy‐to‐learn samples. The findings by various researchers are summarized to a logical end, and various possibilities and challenges for future directions in research are outlined.

Block-Sparse Coding-Based Machine Learning Approach for Dependable Device-Free Localization in IoT Environment

Device-free localization (DFL) locates targets without equipping with wireless devices or tag under the Internet-of-Things (IoT) architectures. As an emerging technology, DFL has spawned extensive applications in the IoT environment, such as intrusion detection, mobile robot localization, and location-based services. Current DFL-related machine learning (ML) algorithms still suffer from low localization accuracy and weak dependability/robustness because the group structure has not been considered in their location estimation, which leads to an undependable process. To overcome these challenges, we propose in this work a dependable block-sparse scheme by particularly considering the group structure of signals. An accurate and robust ML algorithm named block-sparse coding with the proximal operator (BSCPO) is proposed for DFL. In addition, a severe Gaussian noise is added in the original sensing signals for preserving network-related privacy as well as improving the dependability of the model. The real-world data-driven experimental results show that the proposed BSCPO achieves robust localization and signal-recovery performance even under severely noisy conditions and outperforms state-of-the-art DFL methods. For single-target localization, BSCPO retains high accuracy when the signal-to-noise ratio exceeds −10 dB. BSCPO is also able to localize accurately under most multitarget localization test cases.

Applications of machine learning in acute care research.

Artificial intelligence has been successfully applied to numerous health care and non‐health care‐related applications and its use in emergency medicine has been expanding. Among its advantages are its speed in decision making and the opportunity for rapid, actionable deduction from unstructured data with that increases with access to larger volumes of data. Artificial intelligence algorithms are currently being applied to enable faster prognosis and diagnosis of diseases and to improve patient outcomes.1,2 Despite the successful application of artificial intelligence, it is still fraught with limitations and “unknowns” pertaining to the fact that a model's accuracy is dependent on the amount of information available for training the model, and the understanding of the complexity presented by current artificial intelligence and machine learning algorithms is often limited in many individuals outside of those involved in the field. This paper reviews the applications of artificial intelligence and machine learning to acute care research and highlights commonly used machine learning techniques, limitations, and potential future applications.

Applications of Artificial Intelligence to Prostate Multiparametric MRI (mpMRI): Current and Emerging Trends.

Prostate carcinoma is one of the most prevalent cancers worldwide. Multiparametric magnetic resonance imaging (mpMRI) is a non-invasive tool that can improve prostate lesion detection, classification, and volume quantification. Machine learning (ML), a branch of artificial intelligence, can rapidly and accurately analyze mpMRI images. ML could provide better standardization and consistency in identifying prostate lesions and enhance prostate carcinoma management. This review summarizes ML applications to prostate mpMRI and focuses on prostate organ segmentation, lesion detection and segmentation, and lesion characterization. A literature search was conducted to find studies that have applied ML methods to prostate mpMRI. To date, prostate organ segmentation and volume approximation have been well executed using various ML techniques. Prostate lesion detection and segmentation are much more challenging tasks for ML and were attempted in several studies. They largely remain unsolved problems due to data scarcity and the limitations of current ML algorithms. By contrast, prostate lesion characterization has been successfully completed in several studies because of better data availability. Overall, ML is well situated to become a tool that enhances radiologists’ accuracy and speed.

Human Feedback and Knowledge Discovery: Towards Cognitive Systems Optimization

Abstract Current computer vision systems, especially those using machine learning techniques are data-hungry and frequently only perform well when dealing with patterns they have seen before. As an alternative, cognitive systems have become a focus of attention for applications that involve complex visual scenes, and in which conditions may vary. In theory, cognitive applications uses current machine learning algorithms, such as deep learning, combined with cognitive abilities that can broadly generalize to many tasks. However, in practice, perceiving the environment and adapting to unforeseen changes remains elusive, especially for real time applications that has to deal with high-dimensional data processing with strictly low latency. The challenge is not only to extract meaningful information from this data, but to gain knowledge and also to discover insight to optimize the performance of the system. We envision to tackle these difficulties by bringing together the best of machine learning and human cognitive capabilities in a collaborative way. For that, we propose an approach based on a combination of Human-in-the-Loop and Knowledge Discovery in which feedback is used to discover knowledge by enabling users to interactively explore and identify useful information so the system can be continuously trained to gain previously unknown knowledge and also generate new insights to improve human decisions.

KAEA: A Novel Three-stage Ensemble Model for Software Defect Prediction

Software defect prediction is a research hotspot in the field of software engineering. However, due to the limitations of current machine learning algorithms, we can’t achieve good effect for defect prediction by only using machine learning algorithms. In previous studies, some researchers used extreme learning machine (ELM) to conduct defect prediction. However, the initial weights and biases of the ELM are determined randomly, which reduces the prediction performance of ELM. Motivated by the idea of search based software engineering, we propose a novel software defect prediction model named KAEA based on kernel principal component analysis (KPCA), adaptive genetic algorithm, extreme learning machine and Adaboost algorithm, which has three main advantages: (1) KPCA can extract optimal representative features by leveraging a nonlinear mapping function; (2) We leverage adaptive genetic algorithm to optimize the initial weights and biases of ELM, so as to improve the generalization ability and prediction capacity of ELM; (3) We use the Adaboost algorithm to integrate multiple ELM basic predictors optimized by adaptive genetic algorithm into a strong predictor, which can further improve the effect of defect prediction. To effectively evaluate the performance of KAEA, we use eleven datasets from large open source projects, and compare the KAEA with four machine learning basic classifiers, ELM and its three variants. The experimental results show that KAEA is superior to these baseline models in most cases.

MORAL ORTHOSES: A NEW APPROACH TO HUMAN AND MACHINE ETHICS

AbstractMachines are increasingly involved in decisions with ethical implications, which require ethical explanations. Current machine learning algorithms are ethically inscrutable, but not in a way very different from human behavior. This article looks at the role of rationality and reasoning in traditional ethical thought and in artificial intelligence, emphasizing the need for some explainability of actions. It then explores Neil Lawrence's embodiment factor as an insightful way of looking at the differences between human and machine intelligence, connecting it to the theological understanding of embodiment, relationality, and personhood. Finally, it proposes the notion of artificial moral orthoses, which could provide ethical explanations for both artificial and human agents, as a more promising unifying approach to human and machine ethics.

Using deep learning with registry linked claims data to predict hospitalization during chemotherapy: Feasibility study.

270 Background: Approximately half of cancer patients undergoing outpatient chemotherapy experience unplanned Emergency Department (ED) visits and Inpatient (IP) stays. Current machine learning algorithms that identify high-risk patients are based on pre-treatment variables which can not detect changes in risk over time. Deep learning recurrent neural networks can model complex longitudinal patient histories. This study tests the feasibility of using an interpretable recurrent neural network to predict a patient’s daily likelihood of ED and unplanned IP stays in the six months following chemotherapy initiation. Methods: Medicare and commercial claims data were linked with cancer registry records for patients in Washington State from 2011 to 2017. The study included patients diagnosed with any primary tumor site, excluding leukemia, and treated with chemotherapy. We used the Reverse Time Attention model (RETAIN) with a 1:10 case-control match and included registry elements; diagnoses, procedures, medication, and utilization pre-and post-chemotherapy initiation. Patients were randomly divided into internal training, validation, and test sets (75%, 10%, 15%). Model accuracy was measured by the areas under the receiver operating curve (ROC) and precision-recall curve (PRC), and the Youden sensitivity and specificity. Results: Of the 15,400 eligible patients; 4,037 (26.2%) visited the ED a median of 1 time (6,080 total visits); 5,116 (33.2%) had a median of 1 IP stay (7,839 total stays). Both models had good predictive accuracy: The top 20 predictors for ED visits included 5 chemotherapy regimes, 12 procedures, and 2 tumor characteristics; IP stays included all chemotherapy regimes. Conclusions: The promising performance of RETAIN supports the possibility of building a tool capable of estimating daily hospitalization risk. However, future research, particularly with alternative data sources, may be required to predict hospitalization in a real time clinical setting. [Table: see text]

MAPPING GLACIER CHANGES USING CLUSTERING TECHNIQUES ON CLOUD COMPUTING INFRASTRUCTURE

Abstract. Climate change and its effects are taking more importance nowadays; and glaciers are one of the most affected ecosystems by that, considering that the energy of Earth’s surface and its temperature may be directly related to glacier temporal changes. Then, the comprehension of glaciers behaviour, by its retreating or melting critical conditions, can be achieved by the analysis of Remote Sensing data, but considering the unprecedented volumes of information currently provided by satellites sensors, we can refer to this analysis as a big data problem. Machine learning techniques have the potential to improve the analysis of this type of data; however, most current machine learning algorithms are unable to properly process such huge volumes of data. In the attempt to overcome the computational limitations related to Remote Sensing Big Data analysis, we implemented the K-Means and Expectation Maximization algorithms, as distributed clustering solutions, exploiting the capabilities of cloud computing infrastructure for processing very large datasets. The solution was developed over the InterCloud Data Mining Package, which is a suite of distributed classification methods, previously employed in hyperspectral image analysis. In this work we extended the functionalities of that package, by making it able to process multispectral images using the aforementioned clustering algorithms. To validate our proposal, we analysed the Ausangate glacier, located on the Andes Mountains, in Peru, by mapping the changes in such environment through a multi-temporal Remote Sensing analysis. Our results and conclusions are focused on the thematic accuracy and the computational performance achieved by our proposed solution. Thematic accuracy was assessed by comparing the automatically detected glacier areas by the clustering approaches against the manually selected ground truth data. We compared the computational load involved in executing the clustering processes sequentially and in a distributed fashion, using a local mode and cluster configuration over a cloud computing infrastructure.

Updates in Human-AI Teams: Understanding and Addressing the Performance/Compatibility Tradeoff

AI systems are being deployed to support human decision making in high-stakes domains such as healthcare and criminal justice. In many cases, the human and AI form a team, in which the human makes decisions after reviewing the AI’s inferences. A successful partnership requires that the human develops insights into the performance of the AI system, including its failures. We study the influence of updates to an AI system in this setting. While updates can increase the AI’s predictive performance, they may also lead to behavioral changes that are at odds with the user’s prior experiences and confidence in the AI’s inferences. We show that updates that increase AI performance may actually hurt team performance. We introduce the notion of the compatibility of an AI update with prior user experience and present methods for studying the role of compatibility in human-AI teams. Empirical results on three high-stakes classification tasks show that current machine learning algorithms do not produce compatible updates. We propose a re-training objective to improve the compatibility of an update by penalizing new errors. The objective offers full leverage of the performance/compatibility tradeoff across different datasets, enabling more compatible yet accurate updates.

The Unlearnable Checkerboard Pattern

Detecting some patterns is a simple task for humans, but nearly impossible for current machine learning algorithms. Here, the "checkerboard" pattern is examined, where human prediction nears 100% and machine prediction drops significantly below 50%.

Machine Learning Algorithms Successfully Identify the Quality of Lay-Person Directed Articles Online [37T

INTRODUCTION: Many patients (over 80%) research their medical condition online, but there is very little available to guide patients in assessing the quality/accuracy of the information encountered online. We applied machine learning algorithms to online material pertaining to the controversial topic of vaginal mesh, with the goal of creating a tool to assess article quality. METHODS: The complete text of the first 200 hits in Google for “vaginal mesh” (ie articles, blogs, advertisements etc.) were independently assessed by two FPMRS fellowship trained academicians for content quality. The Naïve Bayes algorithm was applied to predict the quality of the article. The articles were randomly partitioned into a training and testing subsets, with the models performance evaluated by its accuracy in assessing the quality of the test subset. RESULTS: Overall 70% of the sources were determined to be of poor quality and reviewers independently agreed 88% of the time on article quality. There was interesting difference in the frequency of commonly used words between the article types. The algorithm correctly classified quality 88% of the time with a 6% “false-negative” and “false-positive” rate. The PPV, NPV, sensitivity and specificity were 91%, 84%, 91% and 84%, respectively. CONCLUSION: We demonstrate that machine learning algorithms can accurately assess the quality of online articles related to vaginal mesh. This is a promising start in the development of tools to guide patients in their independent research. Future directions will be to refine our current machine learning algorithms and to expand the learning to a greater breadth of medical topics.

Transductive Transfer Learning-Based Spectrum Optimization for Resource Reservation in Seven-Core Elastic Optical Networks

In space-division multiplexing elastic optical networks (SDM-EONs), it is important to handle the complex resource optimization (RO) problem due to the coexistence of the requests that require immediate reservation (IR) and those that can be reserved in advance (AR). The introduction of artificial intelligence, especially machine learning algorithms, has made it possible to design more effective RO strategies. However, the model trained by current machine learning algorithms is only suitable for specific scenarios, and cannot be applied to new scenarios, resulting in the need to retrain the models in the new scenario and increase the training costs. Transfer learning (TL) can solve this problem by collecting a small amount of training data in the new scenario and strengthening the existing related models. This paper proposes a RO strategy for resource reservation based on TL in SDM-EONs. If the AR requests fail to reserve resources, TL will be used to predict the spectrum defragmentation time to complete the resource optimization before their start time. For the IR requests, they will occupy the low-level AR requests with the latest start time. Simulation results indicate that the proposed algorithm can decrease the probability for resource reservation failure and improve the resource utilization.

Predicting clinically promising therapeutic hypotheses using tensor factorization

BackgroundDetermining which target to pursue is a challenging and error-prone first step in developing a therapeutic treatment for a disease, where missteps are potentially very costly given the long-time frames and high expenses of drug development. With current informatics technology and machine learning algorithms, it is now possible to computationally discover therapeutic hypotheses by predicting clinically promising drug targets based on the evidence associating drug targets with disease indications. We have collected this evidence from Open Targets and additional databases that covers 17 sources of evidence for target-indication association and represented the data as a tensor of 21,437 × 2211 × 17.ResultsAs a proof-of-concept, we identified examples of successes and failures of target-indication pairs in clinical trials across 875 targets and 574 disease indications to build a gold-standard data set of 6140 known clinical outcomes. We designed and executed three benchmarking strategies to examine the performance of multiple machine learning models: Logistic Regression, LASSO, Random Forest, Tensor Factorization and Gradient Boosting Machine. With 10-fold cross-validation, tensor factorization achieved AUROC = 0.82 ± 0.02 and AUPRC = 0.71 ± 0.03. Across multiple validation schemes, this was comparable or better than other methods.ConclusionIn this work, we benchmarked a machine learning technique called tensor factorization for the problem of predicting clinical outcomes of therapeutic hypotheses. Results have shown that this method can achieve equal or better prediction performance compared with a variety of baseline models. We demonstrate one application of the method to predict outcomes of trials on novel indications of approved drug targets. This work can be expanded to targets and indications that have never been clinically tested and proposing novel target-indication hypotheses. Our proposed biologically-motivated cross-validation schemes provide insight into the robustness of the prediction performance. This has significant implications for all future methods that try to address this seminal problem in drug discovery.

Estimation of wheat planting date using machine learning algorithms based on available climate data

Agricultural applications supported with information technologies increase plant production, protect soil and reduce labor, which is crucial for sustainable agriculture. Impacts of planting dates on production are very well known. In the current study machine learning algorithms have been used in determining planting date. The proposed method aims to help farmers to obtain higher yield providing them with accurate planting date. For this purpose, metereological data was used as an input. For each year, metereological information (Daily Maximum Air Temperature, Daily Relative Humudity, Daily Average Air Temperature, Daily Minimum Air Temperature and Daily Precipitation) in the first 300 days were used to determine three different planting dates; early, normal and late for wheat crop. For estimation of planting date, classification algorithms of k Nearest Neighbor (kNN), Support Vector Machine (SVM) and Decisions Trees were used. Performances of different algorithms were calculated with leave one out cross validation approach. In order to eleminate extremely high processing time because of high dimension of the data set and improve estimation performance, genetic algorithm was used to reduce the number of features. For the estimations performed using both all features and also the features selected with genetic algorithm the highest accuracies were obtained using kNN method with classification accuracy rates of 37% and 92%, respectively. Overall, the results showed that wheat planting date could be determined successfully from climate information obtained in the first 300 days with the help of machine learning techniques combined with feature selection using genetic algorithm, which will prevent low productivity, financial and labor loss as a result of inaccurate planting date.

Analysis of Machine Learning Algorithms in Health Care to Predict Heart Disease

This article describes how healthcare organizations is growing increasingly and are the potential beneficiary users of the data that is generated and gathered. From hospitals to clinics, data and analytics can be a very powerful tool that can improve patient care and satisfaction with efficiency. In developing countries, cardiovascular diseases have a huge impact on increasing death rates and are expected by the end of 2020 in spite of the best clinical practices. The current Machine Learning (ml) algorithms are adapted to estimate the heart disease risks in middle aged patients. Hence, to predict the heart diseases a detailed analysis is made in this research work by taking into account the angiographic heart disease status (i.e. ≥ 50% diameter narrowing). Deep Neural Network (DNN), Extreme Learning Machine (elm), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) learning algorithm (with linear and polynomial kernel functions) are considered in this work. The accuracy and results of these algorithms are analyzed by comparing the effectiveness among them.

A review on microelectrode recording selection of features for machine learning in deep brain stimulation surgery for Parkinson’s disease

ObjectiveThis study seeks to systematically review the selection of features and algorithms for machine learning and automation in deep brain stimulation surgery (DBS) for Parkinson’s disease. This will assist in consolidating current knowledge and accuracy levels to allow greater understanding and research to be performed in automating this process, which could lead to improved clinical outcomes. MethodsA systematic literature review search was conducted for all studies that utilized machine learning and DBS in Parkinson’s disease. ResultsTen studies were identified from 2006 utilizing machine learning in DBS surgery for Parkinson’s disease. Different combinations of both spike independent and spike dependent features have been utilized with different machine learning algorithms to attempt to delineate the subthalamic nucleus (STN) and its surrounding structures. ConclusionThe state-of-the-art algorithms achieve good accuracy and error rates with relatively short computing time, however, the currently achievable accuracy is not sufficiently robust enough for clinical practice. Moreover, further research is required for identifying subterritories of the STN. SignificanceThis is a comprehensive summary of current machine learning algorithms that discriminate the STN and its adjacent structures for DBS surgery in Parkinson’s disease.

Optimal neural inference of stimulus intensities

In natural data, the class and intensity of stimuli are correlated. Current machine learning algorithms ignore this ubiquitous statistical property of stimuli, usually by requiring normalized inputs. From a biological perspective, it remains unclear how neural circuits may account for these dependencies in inference and learning. Here, we use a probabilistic framework to model class-specific intensity variations, and we derive approximate inference and online learning rules which reflect common hallmarks of neural computation. Concretely, we show that a neural circuit equipped with specific forms of synaptic and intrinsic plasticity (IP) can learn the class-specific features and intensities of stimuli simultaneously. Our model provides a normative interpretation of IP as a critical part of sensory learning and predicts that neurons can represent nontrivial input statistics in their excitabilities. Computationally, our approach yields improved statistical representations for realistic datasets in the visual and auditory domains. In particular, we demonstrate the utility of the model in estimating the contrastive stress of speech.