Mining a Small Medical Data Set by Integrating the Decision Tree and t-test

[[abstract]]Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]FI

Applied Deep Learning: Case Studies in Computer Vision and Natural Language Processing

Deep learning has proved to be successful for many computer vision and natural language processing applications. In this dissertation, three studies have been conducted to show the efficacy of deep learning models for computer vision and natural language processing. In the first study, an efficient deep learning model was proposed for seagrass scar detection in multispectral images which produced robust, accurate scars mappings. In the second study, an arithmetic deep learning model was developed to fuse multi-spectral images collected at different times with different resolutions to generate high-resolution images for downstream tasks including change detection, object detection, and land cover classification. In addition, a super-resolution deep model was implemented to further enhance remote sensing images. In the third study, a deep learning-based framework was proposed for fact-checking on social media to spot fake scientific news. The framework leveraged deep learning, information retrieval, and natural language processing techniques to retrieve pertinent scholarly papers for given scientific news and evaluate the credibility of the news

[PDF] from annalsofrscb.ro Predictive Analytics for Sentiment Classification of Social Media Data Using Deep Neural Network

A huge amount of user-generated data in the form of tweets or reviews on social media can be collected and analyzed for making informed decisions. This paper uses the novel deep learning model, namely the Elite Opposition-based Bat Algorithm for Deep Neural Network (EOBA-DNN) for performing polarity classification of the social media data. The proposed method includes three major steps, such as preprocessing, term weighting, and sentiment classification for identifying the polarity of the data. The results show that the EOBA-DNN outperforms other existing algorithms with improved accuracy for Sentiment Classification

Online content clustering using variant K-Means Algorithms

Thesis (MTech)--Cape Peninsula University of Technology, 2019We live at a time when so much information is created. Unfortunately, much of the information is redundant. There is a huge amount of online information in the form of news articles that discuss similar stories. The number of articles is projected to grow. The growth makes it difficult for a person to process all that information in order to update themselves on a subject matter. There is an overwhelming amount of similar information on the internet. There is need for a solution that can organize this similar information into specific themes. The solution is a branch of Artificial intelligence (AI) called machine learning (ML) using clustering algorithms. This refers to clustering groups of information that is similar into containers. When the information is clustered people can be presented with information on their subject of interest, grouped together. The information in a group can be further processed into a summary. This research focuses on unsupervised learning. Literature has it that K-Means is one of the most widely used unsupervised clustering algorithm. K-Means is easy to learn, easy to implement and is also efficient. However, there is a horde of variations of K-Means. The research seeks to find a variant of K-Means that can be used with an acceptable performance, to cluster duplicate or similar news articles into correct semantic groups. The research is an experiment. News articles were collected from the internet using gocrawler. gocrawler is a program that takes Universal Resource Locators (URLs) as an argument and collects a story from a website pointed to by the URL. The URLs are read from a repository. The stories come riddled with adverts and images from the web page. This is referred to as a dirty text. The dirty text is sanitized. Sanitization is basically cleaning the collected news articles. This includes removing adverts and images from the web page. The clean text is stored in a repository, it is the input for the algorithm. The other input is the K value. All K-Means based variants take K value that defines the number of clusters to be produced. The stories are manually classified and labelled. The labelling is done to check the accuracy of machine clustering. Each story is labelled with a class to which it belongs. The data collection process itself was not unsupervised but the algorithms used to cluster are totally unsupervised. A total of 45 stories were collected and 9 manual clusters were identified. Under each manual cluster there are sub clusters of stories talking about one specific event. The performance of all the variants is compared to see the one with the best clustering results. Performance was checked by comparing the manual classification and the clustering results from the algorithm. Each K-Means variant is run on the same set of settings and same data set, that is 45 stories. The settings used are, • Dimensionality of the feature vectors, • Window size, • Maximum distance between the current and predicted word in a sentence, • Minimum word frequency, • Specified range of words to ignore, • Number of threads to train the model. • The training algorithm either distributed memory (PV-DM) or distributed bag of words (PV-DBOW), • The initial learning rate. The learning rate decreases to minimum alpha as training progresses, • Number of iterations per cycle, • Final learning rate, • Number of clusters to form, • The number of times the algorithm will be run, • The method used for initialization. The results obtained show that K-Means can perform better than K-Modes. The results are tabulated and presented in graphs in chapter six. Clustering can be improved by incorporating Named Entity (NER) recognition into the K-Means algorithms. Results can also be improved by implementing multi-stage clustering technique. Where initial clustering is done then you take the cluster group and further cluster it to achieve finer clustering results

Semantic Models for Machine Learning

In this thesis we present approaches to the creation and usage of semantic models by the analysis of the data spread in the feature space. We aim to introduce the general notion of using feature selection techniques in machine learning applications. The applied approaches obtain new feature directions on data, such that machine learning applications would show an increase in performance. We review three principle methods that are used throughout the thesis. Firstly Canonical Correlation Analysis (CCA), which is a method of correlating linear relationships between two multidimensional variables. CCA can be seen as using complex labels as a way of guiding feature selection towards the underlying semantics. CCA makes use of two views of the same semantic object to extract a representation of the semantics. Secondly Partial Least Squares (PLS), a method similar to CCA. It selects feature directions that are useful for the task at hand, though PLS only uses one view of an object and the label as the corresponding pair. PLS could be thought of as a method that looks for directions that are good for distinguishing the different labels. The third method is the Fisher kernel. A method that aims to extract more information of a generative model than simply by their output probabilities. The aim is to analyse how the Fisher score depends on the model and which aspects of the model are important in determining the Fisher score. We focus our theoretical investigation primarily on CCA and its kernel variant. Providing a theoretical analysis of the method's stability using Rademacher complexity, hence deriving the error bound for new data. We conclude the thesis by applying the described approaches to problems in the various fields of image, text, music application and medical analysis, describing several novel applications on relevant real-world data. The aim of the thesis is to provide a theoretical understanding of semantic models, while also providing a good application foundation on how these models can be practically used

Unsupervised Graph-Based Similarity Learning Using Heterogeneous Features.

Relational data refers to data that contains explicit relations among objects. Nowadays, relational data are universal and have a broad appeal in many different application domains. The problem of estimating similarity between objects is a core requirement for many standard Machine Learning (ML), Natural Language Processing (NLP) and Information Retrieval (IR) problems such as clustering, classiffication, word sense disambiguation, etc. Traditional machine learning approaches represent the data using simple, concise representations such as feature vectors. While this works very well for homogeneous data, i.e, data with a single feature type such as text, it does not exploit the availability of dfferent feature types fully. For example, scientic publications have text, citations, authorship information, venue information. Each of the features can be used for estimating similarity. Representing such objects has been a key issue in efficient mining (Getoor and Taskar, 2007). In this thesis, we propose natural representations for relational data using multiple, connected layers of graphs; one for each feature type. Also, we propose novel algorithms for estimating similarity using multiple heterogeneous features. Also, we present novel algorithms for tasks like topic detection and music recommendation using the estimated similarity measure. We demonstrate superior performance of the proposed algorithms (root mean squared error of 24.81 on the Yahoo! KDD Music recommendation data set and classiffication accuracy of 88% on the ACL Anthology Network data set) over many of the state of the art algorithms, such as Latent Semantic Analysis (LSA), Multiple Kernel Learning (MKL) and spectral clustering and baselines on large, standard data sets.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/89824/1/mpradeep_1.pd

An enhanced binary bat and Markov clustering algorithms to improve event detection for heterogeneous news text documents

Event Detection (ED) works on identifying events from various types of data. Building an ED model for news text documents greatly helps decision-makers in various disciplines in improving their strategies. However, identifying and summarizing events from such data is a non-trivial task due to the large volume of published heterogeneous news text documents. Such documents create a high-dimensional feature space that influences the overall performance of the baseline methods in ED model. To address such a problem, this research presents an enhanced ED model that includes improved methods for the crucial phases of the ED model such as Feature Selection (FS), ED, and summarization. This work focuses on the FS problem by automatically detecting events through a novel wrapper FS method based on Adapted Binary Bat Algorithm (ABBA) and Adapted Markov Clustering Algorithm (AMCL), termed ABBA-AMCL. These adaptive techniques were developed to overcome the premature convergence in BBA and fast convergence rate in MCL. Furthermore, this study proposes four summarizing methods to generate informative summaries. The enhanced ED model was tested on 10 benchmark datasets and 2 Facebook news datasets. The effectiveness of ABBA-AMCL was compared to 8 FS methods based on meta-heuristic algorithms and 6 graph-based ED methods. The empirical and statistical results proved that ABBAAMCL surpassed other methods on most datasets. The key representative features demonstrated that ABBA-AMCL method successfully detects real-world events from Facebook news datasets with 0.96 Precision and 1 Recall for dataset 11, while for dataset 12, the Precision is 1 and Recall is 0.76. To conclude, the novel ABBA-AMCL presented in this research has successfully bridged the research gap and resolved the curse of high dimensionality feature space for heterogeneous news text documents. Hence, the enhanced ED model can organize news documents into distinct events and provide policymakers with valuable information for decision making