scholarly journals Scalable hierarchical clustering by composition rank vector encoding and tree structure

2020 ◽  
Author(s):  
Xiao Lai ◽  
Pu Tian
Keyword(s):  
Machine Learning ◽  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
High Dimensional Data ◽  
Machine Learning Algorithms ◽  
Tree Structure ◽  
Supervised Machine Learning ◽  
High Dimensional ◽  
Rank Vector ◽  
Nonlinear Correlations

AbstractSupervised machine learning, especially deep learning based on a wide variety of neural network architectures, have contributed tremendously to fields such as marketing, computer vision and natural language processing. However, development of un-supervised machine learning algorithms has been a bottleneck of artificial intelligence. Clustering is a fundamental unsupervised task in many different subjects. Unfortunately, no present algorithm is satisfactory for clustering of high dimensional data with strong nonlinear correlations. In this work, we propose a simple and highly efficient hierarchical clustering algorithm based on encoding by composition rank vectors and tree structure, and demonstrate its utility with clustering of protein structural domains. No record comparison, which is an expensive and essential common step to all present clustering algorithms, is involved. Consequently, it achieves linear time and space computational complexity hierarchical clustering, thus applicable to arbitrarily large datasets. The key factor in this algorithm is definition of composition, which is dependent upon physical nature of target data and therefore need to be constructed case by case. Nonetheless, the algorithm is general and applicable to any high dimensional data with strong nonlinear correlations. We hope this algorithm to inspire a rich research field of encoding based clustering well beyond composition rank vector trees.

scholarly journals Improving the Accuracy of Convolutional Neural Networks by Identifying and Removing Outlier Images in Datasets Using t-SNE

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 662 ◽  
Author(s):  
Husein Perez ◽  
Joseph H. M. Tah
Keyword(s):  
Machine Learning ◽  
Density Distribution ◽  
Image Classification ◽  
High Dimensional Data ◽  
Supervised Machine Learning ◽  
Learning Problems ◽  
High Dimensional ◽  
Feature Engineering ◽  
Outlier Data

In the field of supervised machine learning, the quality of a classifier model is directly correlated with the quality of the data that is used to train the model. The presence of unwanted outliers in the data could significantly reduce the accuracy of a model or, even worse, result in a biased model leading to an inaccurate classification. Identifying the presence of outliers and eliminating them is, therefore, crucial for building good quality training datasets. Pre-processing procedures for dealing with missing and outlier data, commonly known as feature engineering, are standard practice in machine learning problems. They help to make better assumptions about the data and also prepare datasets in a way that best expose the underlying problem to the machine learning algorithms. In this work, we propose a multistage method for detecting and removing outliers in high-dimensional data. Our proposed method is based on utilising a technique called t-distributed stochastic neighbour embedding (t-SNE) to reduce high-dimensional map of features into a lower, two-dimensional, probability density distribution and then use a simple descriptive statistical method called interquartile range (IQR) to identifying any outlier values from the density distribution of the features. t-SNE is a machine learning algorithm and a nonlinear dimensionality reduction technique well-suited for embedding high-dimensional data for visualisation in a low-dimensional space of two or three dimensions. We applied this method on a dataset containing images for training a convolutional neural network model (ConvNet) for an image classification problem. The dataset contains four different classes of images: three classes contain defects in construction (mould, stain, and paint deterioration) and a no-defect class (normal). We used the transfer learning technique to modify a pre-trained VGG-16 model. We used this model as a feature extractor and as a benchmark to evaluate our method. We have shown that, when using this method, we can identify and remove the outlier images in the dataset. After removing the outlier images from the dataset and re-training the VGG-16 model, the results have also shown that the accuracy of the classification has significantly improved and the number of misclassified cases has also dropped. While many feature engineering techniques for handling missing and outlier data are common in predictive machine learning problems involving numerical or categorical data, there is little work on developing techniques for handling outliers in high-dimensional data which can be used to improve the quality of machine learning problems involving images such as ConvNet models for image classification and object detection problems.

scholarly journals Assessment of supervised machine learning for atmospheric retrieval of exoplanets

2020 ◽  
Vol 496 (1) ◽  
pp. 269-281
Author(s):  
Matthew C Nixon ◽  
Nikku Madhusudhan
Keyword(s):  
Machine Learning ◽  
Transmission Spectrum ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
High Dimensional ◽  
Dimensional Parameter ◽  
Trade Offs ◽  
Fully Bayesian ◽  
Hot Jupiter

ABSTRACT Atmospheric retrieval of exoplanets from spectroscopic observations requires an extensive exploration of a highly degenerate and high-dimensional parameter space to accurately constrain atmospheric parameters. Retrieval methods commonly conduct Bayesian parameter estimation and statistical inference using sampling algorithms such as Markov chain Monte Carlo or Nested Sampling. Recently several attempts have been made to use machine learning algorithms either to complement or to replace fully Bayesian methods. While much progress has been made, these approaches are still at times unable to accurately reproduce results from contemporary Bayesian retrievals. The goal of this work is to investigate the efficacy of machine learning for atmospheric retrieval. As a case study, we use the Random Forest supervised machine learning algorithm which has been applied previously with some success for atmospheric retrieval of the hot Jupiter WASP-12b using its near-infrared transmission spectrum. We reproduce previous results using the same approach and the same semi-analytic models, and subsequently extend this method to develop a new algorithm that results in a closer match to a fully Bayesian retrieval. We combine this new method with a fully numerical atmospheric model and demonstrate excellent agreement with a Bayesian retrieval of the transmission spectrum of another hot Jupiter, HD 209458b. Despite this success, and achieving high computational efficiency, we still find that the machine learning approach is computationally prohibitive for high-dimensional parameter spaces that are routinely explored with Bayesian retrievals with modest computational resources. We discuss the trade-offs and potential avenues for the future.

scholarly journals Survey on Clustering High-Dimensional data using Hubness

2020 ◽  
pp. 01-07
Author(s):  
Miss. Archana Chaudahri ◽  
Mr. Nilesh Vani
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
High Dimensional Data ◽  
Algorithm Design ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
K Nearest Neighbor ◽  
Weighted Voting ◽  
Conventional Machine

Most data of interest today in data-mining applications is complex and is usually represented by many different features. Such high-dimensional data is by its very nature often quite difficult to handle by conventional machine-learning algorithms. This is considered to be an aspect of the well known curse of dimensionality. Consequently, high-dimensional data needs to be processed with care, which is why the design of machine-learning algorithms needs to take these factors into account. Furthermore, it was observed that some of the arising high-dimensional properties could in fact be exploited in improving overall algorithm design. One such phenomenon, related to nearest-neighbor learning methods, is known as hubness and refers to the emergence of very influential nodes (hubs) in k-nearest neighbor graphs. A crisp weighted voting scheme for the k-nearest neighbor classifier has recently been proposed which exploits this notion.

scholarly journals Cross Breed Clustering Algorithm for High Dimensional Data

2019 ◽  
Vol 9 (1) ◽  
pp. 5049-5052
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Growing Domain ◽  
Present World

Clustering plays a major role in machine learning and also in data mining. Deep learning is fast growing domain in present world. Improving the quality of the clustering results by adopting the deep learning algorithms. Many clustering algorithm process various datasets to get the better results. But for the high dimensional data clustering is still an issue to process and get the quality clustering results with the existing clustering algorithms. In this paper, the cross breed clustering algorithm for high dimensional data is utilized. Various datasets are used to get the results.

scholarly journals Dynamic Analytics and Forecasting Model for Covid-19 Using Machine Learning Algorithms

Webology ◽  
2021 ◽  
Vol 18 (05) ◽  
pp. 1212-1225
Author(s):  
Siva C ◽  
Maheshwari K.G ◽  
Nalinipriya G ◽  
Priscilla Mary J
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Prediction Models ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Medical Emergency ◽  
Data Set

In our day to day life, the availability of correctly labelled data as well as handling of categorical data are mostly acknowledged as two main challenges in dynamic analysis. Therefore, clustering techniques are applied on unlabelled data to group them in accordance with the homogeneity. There are many prediction methods that are being popularly used in handling forecasting problems in real time environment. The outbreak of coronavirus disease (COVID19)-2019 creates the need for a medical emergency of worldwide concern with a rapidly high danger of open out and strike the entire world. Recently, the ML prediction models were used in many real time applications which necessitate the identification and categorization for real time environment. In medical field Prediction models are vital role to obtain observations of spread and significances of infectious diseases. Machine learning related forecasting mechanisms have showed their importance to develop the decision making on the upcoming course of actions. The K-means algorithm and hierarchy were applied directly on the renewed dataset using R programming language to create the covid patient cluster. Confirmed Covid patients count are passed to Prophet package, then the prophet model has been created. This forecasts model predicts the future covid count, which is essential for the clinical and healthcare leaders to make the appropriate measures in advance. The results of the experiments indicate that the quality of Hierarchical clustering outperforms than the K-Means clustering algorithm in the structured structured dataset. Thus, the prediction model also used to support model predictions help for the officials to take timely actions and make decisions to contain the COVID-19 dilemma. This work concludes Hierarchical clustering algorithm is the best model for clustering the covid data set obtained from world health organization (WHO).

Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets

2020 ◽  
Vol 14 (2) ◽  
pp. 140-159
Author(s):  
Anthony-Paul Cooper ◽  
Emmanuel Awuni Kolog ◽  
Erkki Sutinen
Keyword(s):  
Machine Learning ◽  
Online Community ◽  
High Volume ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Social Media Data ◽  
Twitter Data ◽  
Resource Intensity ◽  
Media Data ◽  
Better Than

This article builds on previous research around the exploration of the content of church-related tweets. It does so by exploring whether the qualitative thematic coding of such tweets can, in part, be automated by the use of machine learning. It compares three supervised machine learning algorithms to understand how useful each algorithm is at a classification task, based on a dataset of human-coded church-related tweets. The study finds that one such algorithm, Naïve-Bayes, performs better than the other algorithms considered, returning Precision, Recall and F-measure values which each exceed an acceptable threshold of 70%. This has far-reaching consequences at a time where the high volume of social media data, in this case, Twitter data, means that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interacts with, and talks about, church. The findings presented in this article offer a way forward for scholars of digital theology to better understand the content of online church discourse.

Sign in / Sign up

Export Citation Format

Share Document