Unsupervised forward selection: a method for eliminating redundant variables.

Today, with the development of technology, the decision-making capabilities of machines have also increased. With their high analytical skills, computers can easily catch points and relationships that may escape the human eye. Thanks to these capabilities, machines are also widely used in the field of health. For example, many machine learning techniques developed on cancer prediction have been successfully applied. Early detection of cancer is crucial to survival. In the early diagnosis of cancer, the rates of drug treatment, chemotherapy or radiotherapy that the person will be exposed to are significantly reduced and the patient gets through this process with the least amount of wear and tear. Gene Expression Cancer RNA-Seq Dataset was used in this study. This data set includes gene expression values of 5 cancer types (BRCA, KIRC, LUAD, LUSC, UCEC). DNA sequences in the dataset were analyzed using k-means and hierarchical clustering algorithms, which are unsupervised machine learning methods. The aim of the study is to develop a usable machine learning model for early detection of cancer at the gene level. Adjusted Rand Index (ARI), Silhouette Score, and Accuracy metrics were used to evaluate the analysis results. The rand index calculates similarity between clusters by counting the binaries assigned to clusters. The adjusted Rand Index is a randomly adjusted version of the Rand Index. The silhouette score indicates how well a data point fits within its own set among separated datasets. The accuracy metric is obtained as a percentage of correctly clustered data points divided by all predictions. Different connection methods are used in the hierarchical clustering algorithm. These are 'complete', 'ward', 'average' and 'single'. As a result of the study, the accuracy in the k-means algorithm was 0.990, the Adjusted Rand Index was 0.79, and the Silhouette Score was 0.14. Looking at the hierarchical clustering, ward performed the best of the four linkage methods, with an ARI score of 0.76 and a silhouette score of 0.13. As a result of the study, the accuracy of in the hierarchical clustering algorithm was 0.999.

The use of no-observed-effect levels (NOLs) and lowest-observed-effect levels (LOELs) assummary statistics for low toxic effects has been criticized recently. In this paper, we explore the regression-based approach for estimating low toxic effects. Key questions with this approach include: (1) is the approach practical with typical toxicity data sets, (2) can low toxic effects be estimated with confidence, (3) what models are appropriate for typical data sets and endpoints, and (4) how do point estimates of low toxic effects (e.g., EC10) compare with their corresponding LOELs and NOELs? Analyses of 198 toxicity data sets (all but 13 unpublished) revealed that: (1) even with a range of models to choose from (three models in the logistic family, probit model, Weibull model), >80% of the data sets did not produce a single adequate model fit; (2) estimates of low toxic effects were often model dependent when an extrapolation beyond the toxicity data was required; (3) confidence intervals can be quite large at 5% effect and lower; (4) of the five models, the three-parameter logistic equation with a steep slope parameter had the best model fit in the majority of the data sets; and (5) 76.9% of the NOELs and 100% of the LOELs were higher than their corresponding 10% effect point estimates from the best-fit model equations, suggesting that NOELs and LOELs are poor indicators of low toxic effects. We believe that the regression-based approach is a better tool than hypothesis testing for estimating low toxic effects. The approach, however, does not produce adequate model fits unless there is an obvious concentration–response relationship and several treatments with partial effects.

Mining frequent patterns has been a topic of active research because it is computationally the most expensive step in association rule discovery. In this paper, we discuss the use of compact data structure design for improving the efficiency of frequent pattern mining. It is based on our work in developing efficient algorithms that outperform the best available frequent pattern algorithms on a number of typical data sets. We discuss improvements to the data structure design that has resulted in faster frequent pattern discovery. The performance of our algorithms is studied by comparing their running times on typical test data sets against the fastest Apriori, Eclat, FP-Growth and OpportuneProject algorithms. We discuss the performance results as well as the strengths and limitations of our algorithms.

The marked degree of heterogeneity in persons with developmental dyslexia has motivated the investigation of possible subtypes. Attempts have proceeded both from theoretical models of reading and the application of unsupervised learning (clustering) methods. Previous cluster analyses of data obtained from persons with reading disabilities have suffered from the inherent limitations of unsupervised learning methods. Specifically, the reliability and stability of cluster solutions have proven difficult to determine. Recent developments in the clustering literature have addressed these concerns by permitting checks on the internal validity of the solution. Resampling methods produce consistent groupings of the data independent of initialization effects, while the gap statistic provides a confidence measure for the determination of the optimal number of clusters present in the data. Combining these methods produces a robust data-driven classification that can be compared with theoretically based subtypes to produce better-informed models of developmental dyslexia. The present study is a novel application of resampling (bootstrap aggregating or bagging) methods and the gap statistic to the subtyping of children with developmental dyslexia. The specific aims of this study are: (1) to illustrate the use of bagging methods and the gap statistic in multivariate data obtained from children with developmental dyslexia; and (2) to compare the bagged clustering thresholded by the gap statistic against the predictions of the double-deficit hypothesis. The double-deficit hypothesis is a prominent theoretical model of developmental dyslexia, which predicts three subtypes: phonological, rate, and phonological-rate impaired readers. Three simulated data sets with known cluster structure were created to check the validity and illustrate the utility of the bagged clustering with the gap statistic in data with known structure. Subsequently, a clinical database of standardized test data (eight tests) from 93 children with developmental dyslexia was clustered using these methods. This procedure was repeated on a database of 93 children without reading disability matched for gender and age as a control. Finally, the clustering was repeated on the entire database of 186 participants. Cluster solutions were obtained for an increasing number of clusters (1-10) and were tested against the null hypothesis that no subtypes were present, i.e. the data represented a single cluster. Four clusters were identified in the children with developmental dyslexia. There was no evidence of significant cluster structure in the children without dyslexia. Two clusters were identified when children with and without reading impairments were considered together. Among the participants with developmental dyslexia, there was evidence of a phonological-deficit cluster, a rapid-naming cluster, and a cluster showing both depressed phonological processing and rapid naming. These accounted for 73 of the 93 participants (78%). All three are predicted by the double-deficit hypothesis. The fourth cluster consisted of children with normal phonological and rapid naming ability incommensurate with their high verbal ability. An analysis of variance with post-hoc multiple comparisons demonstrated that the phonological, rapid-naming, and double deficit clusters did not differ significantly in age, but the fourth cluster was comprised of significantly older children. The mixed data set revealed two clusters. One cluster consisted almost entirely of the double-deficit and phonological subtypes. The other consisted of the participants without dyslexia and the children with dyslexia demonstrating either a single rapid naming deficit or standardized test scores in the normal range. A silhouette analysis indicated that the four-cluster solution for the children with developmental dyslexia was superior to the two-cluster solution obtained for the entire data set. The study provides support for the presence of distinct subtypes in children with developmental dyslexia and for the double-deficit hypothesis. Specifically, this study finds three subtypes predicted by the double-deficit hypothesis without the assumption of an a priori theoretical model of reading. Taken together, these subtypes account for 79% of the participants with dyslexia. Further, the percentages of children in each subtype are in good agreement with previous studies. The participants in the subtype not predicted by the double-deficit hypothesis were significantly older than the other three groups. Recent advances in unsupervised learning can be expected to aid the improvement and refinement of the definition of developmental dyslexia. If reliable and consistent subtypes can be identified among persons with developmental dyslexia, it is reasonable to assume that diagnostic and intervention efforts will be greatly improved.

This study aimed to evaluate the prognosis of patients with COVID-19 and hypertension who were treated with angiotensin-converting enzyme inhibitor (ACEI)/angiotensin receptor B (ARB) drugs and to identify key features affecting patient prognosis using an unsupervised learning method. A large-scale clinical dataset, including patient information, medical history, and laboratory test results, was collected. Two hundred patients with COVID-19 and hypertension were included. After cluster analysis, patients were divided into good and poor prognosis groups. The unsupervised learning method was used to evaluate clinical characteristics and prognosis, and patients were divided into different prognosis groups. The improved wild dog optimization algorithm (IDOA) was used for feature selection and cluster analysis, followed by the IDOA-k-means algorithm. The impact of ACEI/ARB drugs on patient prognosis and key characteristics affecting patient prognosis were also analysed. Key features related to prognosis included baseline information and laboratory test results, while clinical symptoms and imaging results had low predictive power. The top six important features were age, hypertension grade, MuLBSTA, ACEI/ARB, NT-proBNP, and high-sensitivity troponin I. These features were consistent with the results of the unsupervised prediction model. A visualization system was developed based on these key features. Using unsupervised learning and the improved k-means algorithm, this study accurately analysed the prognosis of patients with COVID-19 and hypertension. The use of ACEI/ARB drugs was found to be a protective factor for poor clinical prognosis. Unsupervised learning methods can be used to differentiate patient populations and assess treatment effects. This study identified important features affecting patient prognosis and developed a visualization system with clinical significance for prognosis assessment and treatment decision-making.

Unsupervised learning methods are effective and suitable tools for damage detection. The main reason for the popularity of these methods in structural health monitoring originates from the fact that the process of learning can be implemented by information of the only normal condition called training data. In contrast, supervised learning methods require information of both normal and current conditions for the process of interest. Because civil engineering structures are expensive and complex, it is not reasonable and economical to impose intentional damage on providing training data. Hence, it is not simple to directly exploit supervised learning techniques in structural health monitoring. To deal with this limitation, this article proposes a novel two-level strategy including three algorithms for using the concepts of both unsupervised learning and supervised learning. The major contribution of this strategy is to consider supervised learning as a validation tool for damage detection. First, the results of damage detection are obtained from two unsupervised learning methods developed by Mahalanobis squared distance and a deep autoencoder neural network in the first two algorithms of the proposed strategy. The main objective is to separate accurate and confusing results of damage detection based on Type I and Type II errors. Second, the confusing results are fed into the third algorithm to train a classifier and compute their classification margins for making the final decision and validating damage detection. The effectiveness and applicability of the proposed strategy are assessed by a numerical concrete beam and an experimental laboratory frame. Results show that this strategy with the aid of the Naive Bayes classifier enables the unsupervised learning methods to make accurate decisions.

Fast synchronization clustering algorithms based on spatial index structures

The successful use of social media to manipulate public opinion via bots and hired individuals to spread (mis)information to unsuspecting users reached alarming levels due to the manipulations during the 2016 US elections and the Brexit deliberations in the UK. Fake interaction such as “liking” and “retweeting” are staged to foster trust in the posts of bots and individuals, which makes it difficult for individuals to detect the posts that are part of greater schemes. We propose an approach based on supervised learning to classify collections of tweets as “organized” when they inhabit premeditated intent and as “organic” otherwise. Features related to users and posting behavior are used to train the classifiers using 851 data sets totaling above 270 million tweets. Further classifiers are trained to assess the effectiveness of the selected features. The random forest algorithm persistently yielded the best results with scores greater than 95% for both accuracy and f-measure. For comparison purposes, unsupervised learning methods were used to cluster the same data sets. The Gaussian Mixture Model clustered [organized vs organic] data set with 99% agreement with the labels. The success of using only behavioral features to detect organized behavior is encouraging.

Seismic data denoising is a critical component of seismic data processing, yet effectively removing erratic noise, characterized by its non-Gaussian distribution and high amplitude, remains a substantial challenge for conventional methods and deep-learning (DL) algorithms. Supervised learning frameworks typically outperform others, but they require pairs of noisy data sets alongside corresponding clean ground truth, which is impractical for real-world seismic data sets. In contrast, unsupervised learning (UL) methods, which do not rely on ground truth during training, often fall short in performance when compared with their supervised or traditional denoising counterparts. Moreover, current unsupervised DL methods fail to address the specific challenges posed by erratic seismic noise adequately. This paper introduces a novel zero-shot unsupervised DL framework designed specifically to mitigate random and erratic noise, with a particular emphasis on blended noise. Drawing inspiration from Noise2Noise (N2N) and data augmentation principles, we develop a robust self-supervised denoising network called robust Noiser2Noiser. Our approach eliminates the need for paired noisy and clean data sets as required by supervised methods or paired noisy data sets as in N2N. Instead, our framework relies solely on the original noisy seismic data set. Our methodology generates two independent recorrupted data sets from the original noisy data set, using one as the input and the other as the training target. Subsequently, we use a DL-based denoiser, denoising convolutional neural network, for training purposes. To address various types of random and erratic noise, the original noisy data set is recorrupted with the same noise type. Detailed explanations for generating training input and target data for blended data are provided. We apply our network to synthetic and real marine data examples, demonstrating significantly improved noise attenuation performance compared with traditional denoising methods and state-of-the-art UL Codes are available on https://github.com/Ji-seismic/N2N_deblending.

Unsupervised learning from EEG data for epilepsy: A systematic literature review.

Image recognition based on unsupervised learning is a worthy direction of research. Inspired by ImageNet and other open source image sets, more and more unsupervised learning methods have been applied to image recognition and achieved good results. Based on the unsupervised learning model based on momentum contrast, the attention mechanism module is introduced, and a new parameter adjustment strategy for learning rate is proposed for further optimization of the algorithm. In the module of attentional mechanism, self-SE attentional mechanism model is proposed by integrating self-attention and SENet models. Batch-size Warmup parameter adjustment strategy is proposed in this paper to further improve the performance of the model. In this paper, the proposed improved module and adjustment strategy are verified by several groups of experiments with CIFAR-100 as data set. Compared with the original method, the accuracy of the unsupervised learning image recognition method with improved modules and adjusted strategies has been improved by about 1.2%.

Aspect-level opinion mining systems suffer from concept coverage problem due to the richness and ambiguity of natural language opinions. Aspects mentioned by review authors can be expressed in various forms, resulting in a potentially large number of missing or incomplete aspects. This work proposes a novel unsupervised method to extract aspects from raw reviews with a broader coverage. Previous research has shown that unsupervised methods based on dependency relations are promising for opinion target extraction (OTE). In this work, we introduce Aspect Network (AspNet), an AspNet that further improves existing OTE methods by providing a new framework for modeling aspects. AspNet represents the general indecomposable atom aspects and their dependency relations in a two-layered, directed, weighted graph, based on which the specific decomposable compound aspects in reviews can be effectively extracted. AspNet is constructed through an unsupervised learning method that starts from a small number of human-defined, domain-dependent aspects, and bootstraps generalization and propagation in a large volume of raw reviews. In summary, the major contributions of this work are twofold: Firstly, the proposed AspNet is a new framework in modeling aspects; secondly, an unsupervised method is proposed to construct AspNet in a bootstrapping manner within raw reviews to learn aspects automatically. Experimental results demonstrate that our proposed OTE method, based on AspNet, can achieve significant gains over baseline methods.

The Unsupervised Distance Learning Framework (UDLF) [1] is a software developed to facilitate the general use and evaluation of novel unsupervised learning methods. These methods aim at post-processing the ranking information for different tasks, being especially useful for multimedia retrieval. The major advantage of UDLF is that it provides a unified and extensive model for implementing different unsupervised methods. Any execution or experiment can be easily conducted by setting a configuration file, no recompilation is required. To evaluate the retrieval results, the framework computes different effectiveness and efficiency measures and it allows for exporting visual output results to external files. UDLF is written in C++, works out of the box, does not require external libraries, and is compatible with different operating systems including Linux, MacOS, and Windows. The source code is publicly available on GitHub under the terms of the GPLv2 license.

Evaluation of the effect of chance correlations on variable selection using Partial Least Squares-Discriminant Analysis

This research article proposes a deep learning framework that addresses two major hurdles in adopting deep learning techniques for solving physics-based problems. One is the requirement of a large data set for training the deep learning (DL) model and another is the consistency of a DL model with the physics of a phenomenon. The framework is generic that can be applied to model a phenomenon in physics if its behavior is known. A semi-supervised physics guided neural network (SPGNN) has been developed based on our framework to demonstrate the concept. SPGNN models the I–V characteristics of gallium nitride based high electron mobility transistors (GaN HEMTs). A two-stage method has been proposed to train a DL model. In the first stage, the DL model is trained via an unsupervised learning method using the analytical physics-based model of a field-effect transistor (FET) as a loss function of the DL model that incorporates the physics of the FET in the DL model. Later, the DL model is fine-tuned with a small set of experimental data in the second stage. Performance of SPGNN has been assessed on various sizes of the data set with 100, 500, 1000, 1500, and 2000 samples. SPGNN significantly reduces the training data requirement by more than 80% and provides better performance than a traditionally trained neural network (TTNN), even for the unseen test data set. SPGNN predicts 32.4% of the unseen test data with less than 1% of error and only 0.4% of the unseen test data with more than 10% of error.