Relevant bibliographies by topics / UCI repository

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'UCI repository'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "UCI repository"

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Macià, Núria, and Ester Bernadó-Mansilla. "Towards UCI+: A mindful repository design." Information Sciences 261 (March 2014): 237–62. http://dx.doi.org/10.1016/j.ins.2013.08.059.

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Chu, Xianghua, Shuxiang Li, Da Gao, Wei Zhao, Jianshuang Cui, and Linya Huang. "A Binary Superior Tracking Artificial Bee Colony with Dynamic Cauchy Mutation for Feature Selection." Complexity 2020 (November 9, 2020): 1–13. http://dx.doi.org/10.1155/2020/8864315.

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This paper aims to propose an improved learning algorithm for feature selection, termed as binary superior tracking artificial bee colony with dynamic Cauchy mutation (BSTABC-DCM). To enhance exploitation capacity, a binary learning strategy is proposed to enable each bee to learn from the superior individuals in each dimension. A dynamic Cauchy mutation is introduced to diversify the population distribution. Ten datasets from UCI repository are adopted as test problems, and the average results of cross-validation of BSTABC-DCM are compared with other seven popular swarm intelligence metaheuri
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Дюк, В. А., И. Г. Малыгин, and В. И. Прицкер. "Vehicle recognition by silhouettes – a three-stage machine learning method in computer vision systems." MORSKIE INTELLEKTUAL`NYE TEHNOLOGII)</msg>, no. 2(56) (June 9, 2022): 162–67. http://dx.doi.org/10.37220/mit.2022.56.2.022.

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В районах морских портов, на морских и сухопутных трассах актуальной является задача учета и контроля различных транспортных средств. Для решения этой задачи всё чаще используются технические системы распознавания таких средств, использующие видеокамеры. Однако видеоизображения по ряду причин не всегда бывают высокого качества. Поэтому теоретический и практический интерес представляет задача распознавания транспортных средств по сильно загрубленным их изображениям – силуэтам. В нашем исследовании используется экспериментальный материал из репозитория данных UCI (UCI Machine Learning Repository
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Lee, Chun-Yao, and Guang-Lin Zhuo. "A Hybrid Whale Optimization Algorithm for Global Optimization." Mathematics 9, no. 13 (2021): 1477. http://dx.doi.org/10.3390/math9131477.

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This paper proposes a hybrid whale optimization algorithm (WOA) that is derived from the genetic and thermal exchange optimization-based whale optimization algorithm (GWOA-TEO) to enhance global optimization capability. First, the high-quality initial population is generated to improve the performance of GWOA-TEO. Then, thermal exchange optimization (TEO) is applied to improve exploitation performance. Next, a memory is considered that can store historical best-so-far solutions, achieving higher performance without adding additional computational costs. Finally, a crossover operator based on t
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P., Ashok, and G. M. Kadhar Nawaz. "Outlier Detection Method on UCI Repository Dataset by Entropy Based Rough K-means." Defence Science Journal 66, no. 2 (2016): 113. http://dx.doi.org/10.14429/dsj.66.9463.

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&lt;p&gt;Rough set theory is used to handle uncertainty and incomplete information by applying two sets, lower and upper approximation. In this paper, the clustering process is improved by adapting the preliminary centroid selection method on rough K-means (RKM) algorithm. The entropy based rough K-means (ERKM) method is developed by adapting entropy based preliminary centroids selection on RKM and executed and also validated by cluster validity indexes. An example shows that the ERKM performs effectively by selection of entropy based preliminary centroid. In addition, Outlier detection is an
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Naz, Mehreen, Kashif Zafar, and Ayesha Khan. "Ensemble Based Classification of Sentiments Using Forest Optimization Algorithm." Data 4, no. 2 (2019): 76. http://dx.doi.org/10.3390/data4020076.

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Feature subset selection is a process to choose a set of relevant features from a high dimensionality dataset to improve the performance of classifiers. The meaningful words extracted from data forms a set of features for sentiment analysis. Many evolutionary algorithms, like the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), have been applied to feature subset selection problem and computational performance can still be improved. This research presents a solution to feature subset selection problem for classification of sentiments using ensemble-based classifiers. It consists o
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Kurniawan, Ilham. "Prediksi Gejala Autism Spectrum Disorders pada Remaja Menggunakan Optimasi Particle Swarm Optimization dan Algoritma Support Vector Machine." INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics 4, no. 2 (2020): 113. http://dx.doi.org/10.51211/itbi.v4i2.1306.

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Abstrak: Telah ada peningkatan prevalensi diagnosis Autism Spectrum Disorder (ASD) secara global selama dekade terakhir. Perkiraan prevalensi ASD yang diperbarui dan keseluruhan di Asia akan membantu para profesional kesehatan untuk mengembangkan strategi kesehatan masyarakat yang relevan. Dalam penelitian ini, mengusulkan metode untuk prediksi gejala ASD menggunakan teknik integrasi seleksi fitur PSO dan algoritma Support Vector Machine. Penelitian ini menggunakan dataset dari UCI repository. Model yang diusulkan meliputi penerapan seleksi fitur menggunakan particle swarm optimization (PSO),
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Yamaguchi, Naoto, Mao Wu, Michinori Nakata, and Hiroshi Sakai. "Application of Rough Set-Based Information Analysis to Questionnaire Data." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 6 (2014): 953–61. http://dx.doi.org/10.20965/jaciii.2014.p0953.

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This article reports an application ofRough Nondeterministic Information Analysis (RNIA)to two data sets. One is the Mushroom data set in the UCI machine leaning repository, and the other is a student questionnaire data set. Even though these data sets include many missing values, we obtained some interesting rules by using ourgetRNIAsoftware tool. This software is powered by theNIS-Apriorialgorithm, and we apply rule generation and question-answering functionalities to data sets with nondeterministic values.
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Zouggar, Souad Taleb, and Abdelkader Adla. "Proposal for Measuring Quality of Decision Trees Partition." International Journal of Decision Support System Technology 9, no. 4 (2017): 16–36. http://dx.doi.org/10.4018/ijdsst.2017100102.

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To compute a partition quality for a decision tree, we propose a new measure called NIM “New Information Measure”. The measure is simpler, provides similar performance, and sometimes outperforms the existing measures used with tree-based methods. The experimental results using the MONITDIAB application (Taleb &amp; Atmani, 2013) and datasets from the UCI repository (Asuncion &amp; Newman, 2007) confirm the classification capabilities of our proposal in comparison to the Shannon measure used with ID3 and C4.5 decision tree methods.
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Mbevi, Rose Mueni, John Kamau, and Faith Mueni Musyoka. "Content Based Approach for Detecting Smishing Messages in Mobile Phones Using an Improved Convolutional Neural Networks Model." African Journal of Empirical Research 6, no. 2 (2025): 188–204. https://doi.org/10.51867/ajernet.6.2.17.

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SMS stands for Short Message Service (SMS). Short messaging service is a text messaging service where a user can send short messages via a mobile device. Short message service has evolved and become very popular as a communication medium in the last decade. It has become a more effective mode of communication compared to email. Unfortunately, smishing (SMS phishing) has emerged as the most common type of spam because traditional detection methods have difficulty understanding the informal nature of these messages. An improved class of CNN-based models targeted at accurate detection of smishing
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Dissertations / Theses on the topic "UCI repository"

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Macha, Annah Sephene. "Towards the establishment and implementation of an institutional repository at the University of Cape Town (UCT): a case study." Master's thesis, University of Cape Town, 2012. http://hdl.handle.net/11427/12077.

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Includes abstract.<br>Includes bibliographical references.<br>The concepts of open access and scholarly publishing are still gaining momentum in Africa, especially South Africa. Increasingly, institutional repositories are being planned and developed by universities throughout the world especially in the first world countries, which have taken the lead. Institutional repositories have developed because of changes in scholarly communication where journal prices are high and libraries are finding it difficult to subscribe to them. Communication technology in the form of the internet brought a so
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McCutcheon, Angela M. "Impact of Publishers’ Policy on Electronic Thesis and Dissertation (ETD) Distribution Options within the United States." Ohio University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1273584209.

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KUMAR, MUNISH. "A COMPARATIVE STUDY OF VARIOUS ML TECHNIQUES FOR HEART DISEASE PREDICTION." Thesis, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/20456.

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Heart is one in every of the most important organs that has a lot of precedence in flesh. It provides the blood to any or all organs of the entire body by pumping it. Heart condition may be a prime root of death within the world. A large quantity of information is collected in medical business associated with heart condition. However, this knowledge isn't mined properly. Prediction of heart diseases in care field is critical work. Several researchers have already been operating within the field of heart condition prediction exploitation some machine learning algorithms. The results
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Tangsripairoj, Songsri. "A growing hierarchical self-organizing map with mining association rules for software repository organization and visualization." 2004. http://digital.library.okstate.edu/etd/umi-okstate-1123.pdf.

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RAJ, ABHISHEK. "CONSUMER PERCEPTION ON UPI." Thesis, 2023. http://dspace.dtu.ac.in:8080/jspui/handle/repository/20208.

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In Simple Words, With the Advent of UPI it is vital for us to get an insight about the effectives and its acceptance among the common people. Also it is Important for the UPI system which was initially inaugurated by the PM Modi in 2016 it was created by NPCL with the help of RBI .IT was supposed to give a boost to the online economy which would farther reduce the black economy and make the Indian Government r have a better tax reserves. In this Study we will get an overview about the use of UPI System and how it is effective in terms of Reliability, Save times, Data Loss, Safety. Also it wou
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Books on the topic "UCI repository"

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Acevedo Saavedra, Maribel, and Ricardo Vilches Vargas. Creación de perfil para investigador en ORCID. Pontificia Universidad Católica de Chile, 2023. http://dx.doi.org/10.7764/inesca.uc.t02.

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Contribuir a la Comunidad UC en la creación y/o actualización de su perfil de investigador en ORCID. Para lograr esto se realizan de manera sistemática talleres a la comunidad universitaria para dar a conocer las funcionalidades de ORCID en el ámbito de la investigación académica y en el marco de la Ciencia Abierta. Aplicando una metodología de trabajo práctico se enseñó la conexión del perfil ORCID a los docentes y la vinculación de este perfil en el Repositorio UC.
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Book chapters on the topic "UCI repository"

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Soares, Carlos. "Is the UCI Repository Useful for Data Mining?" In Progress in Artificial Intelligence. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24580-3_28.

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Aufschläger, Robert, Sebastian Wilhelm, Michael Heigl, and Martin Schramm. "ClustEm4Ano: Clustering Text Embeddings of Nominal Textual Attributes for Microdata Anonymization." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-83472-1_9.

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Abstract This work introduces , an anonymization pipeline that can be used for generalization and suppression-based anonymization of nominal textual tabular data. It automatically generates value generalization hierarchies (VGHs) that, in turn, can be used to generalize attributes in quasi-identifiers. The pipeline leverages embeddings to generate semantically close value generalizations through iterative clustering. We applied KMeans and Hierarchical Agglomerative Clustering on 13 different predefined text embeddings (both open and closed-source (via APIs)). Our approach is experimentally tes
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Padierna Luis C., González Martha B., and Romero Leoncio A. "PAC-Means: clustering algorithm based on c-Means technique and associative memories." In Ambient Intelligence and Smart Environments. IOS Press, 2012. https://doi.org/10.3233/978-1-61499-080-2-37.

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In this study a partitional clustering technique is proposed. Our proposal is a variant of the c-Means algorithm that replaces its traditional minimum-distance classifier by a classifier based on associative memories. The variant was compared against the original version by applying both techniques to three datasets belonging to the UCI Machine Learning Repository: Iris, Wine and Pima Indian Diabetes. As a comparison criterion, an intracluster-spread index was used. Results obtained in experimental tests show that, when applied to certain databases, the PAC-Means technique overcomes to the c-M
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Mittal, ​Mamta, ​Gopi Battineni, Bhimavarapu Usharani, and Lalit Mohan Goyal. "Text Clustering in Python." In Text Analysis with Python: A Research Oriented Guide. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/9789815049602122010007.

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In this chapter, we learn about clustering and how document and text clustering can be performed. This chapter explains the real-time applications of text clustering and the differences between soft and hard clustering types. The clustering algorithms, including KNN, hierarchical and Fuzzy clustering, were used . Fuzzy clustering or soft clustering types can add better value performance-wise than the other two clustering algorithms. Besides, we also presented how to conduct text clustering in python using unsupervised machine learning techniques. To explain this in detail, the IRIS dataset is
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Setiawan, Noor Akhmad. "Fuzzy Decision Support System for Coronary Artery Disease Diagnosis Based on Rough Set Theory." In Fuzzy Systems. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1908-9.ch055.

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The objective of this research is to develop an evidence based fuzzy decision support system for the diagnosis of coronary artery disease. The development of decision support system is implemented based on three processing stages: rule generation, rule selection and rule fuzzification. Rough Set Theory (RST) is used to generate the classification rules from training data set. The training data are obtained from University California Irvine (UCI) data repository. Rule selection is conducted by transforming the rules into a decision table based on unseen data set. Furthermore, RST attributes red
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Kumar, Amit, and Bikash Kanti Sarkar. "Performance Analysis of Nature-Inspired Algorithms-Based Bayesian Prediction Models for Medical Data Sets." In Advances in Computer and Electrical Engineering. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3531-7.ch007.

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Research in medical data prediction has become an important classification problem due to its domain specificity, voluminous, and class imbalanced nature. In this chapter, four well-known nature-inspired algorithms, namely genetic algorithms (GA), genetic programming (GP), particle swarm optimization (PSO), and ant colony optimization (ACO), are used for feature selection in order to enhance the classification performances of medical data using Bayesian classifier. Naïve Bayes is most widely used Bayesian classifier in automatic medical diagnostic tools. In total, 12 real-world medical domain
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Bidi, Noria, and Zakaria Elberrichi. "Best Features Selection for Biomedical Data Classification Using Seven Spot Ladybird Optimization Algorithm." In Cognitive Analytics. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch021.

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This article presents a new adaptive algorithm called FS-SLOA (Feature Selection-Seven Spot Ladybird Optimization Algorithm) which is a meta-heuristic feature selection method based on the foraging behavior of a seven spot ladybird. The new efficient technique has been applied to find the best subset features, which achieves the highest accuracy in classification using three classifiers: the Naive Bayes (NB), the Nearest Neighbors (KNN) and the Support Vector Machine (SVM). The authors' proposed approach has been experimented on four well-known benchmark datasets (Wisconsin Breast cancer, Pima
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Bisht, Shilpi, and Neeraj Bisht. "A Machine Learning Approach for Detecting Autism Spectrum Disorder Using Classifier Techniques." In Advances in Computational Intelligence and Robotics. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-2443-8.ch001.

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Autism spectrum disorder (ASD) is a neurological developmental disorder that results in infirmity in social behaviour and social communication. Autism is identifiable at any stage of life, but symptoms usually appear in the first two years. This chapter deals with ASD at three different levels: child, adolescent, and adult. For this purpose, the authors have used a dataset from the UCI repository submitted by Fadi Fayez Thabtah, which has 20 features. They proposed new supervised machine learning models to predict the possibility of autism disorder at the adult stage through child, adolescent,
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Kumar, Amit, and Bikash Kanti Sarkar. "Performance Analysis of Nature-Inspired Algorithms-Based Bayesian Prediction Models for Medical Data Sets." In Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8048-6.ch044.

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Research in medical data prediction has become an important classification problem due to its domain specificity, voluminous, and class imbalanced nature. In this chapter, four well-known nature-inspired algorithms, namely genetic algorithms (GA), genetic programming (GP), particle swarm optimization (PSO), and ant colony optimization (ACO), are used for feature selection in order to enhance the classification performances of medical data using Bayesian classifier. Naïve Bayes is most widely used Bayesian classifier in automatic medical diagnostic tools. In total, 12 real-world medical domain
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Alaoui, Abdiya, and Zakaria Elberrichi. "Neuronal Communication Genetic Algorithm-Based Inductive Learning." In Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8048-6.ch013.

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The development of powerful learning strategies in the medical domain constitutes a real challenge. Machine learning algorithms are used to extract high-level knowledge from medical datasets. Rule-based machine learning algorithms are easily interpreted by humans. To build a robust rule-based algorithm, a new hybrid metaheuristic was proposed for the classification of medical datasets. The hybrid approach uses neural communication and genetic algorithm-based inductive learning to build a robust model for disease prediction. The resulting classification models are characterized by good predicti
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Conference papers on the topic "UCI repository"

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Ashok, P., and G. M. Kadhar Nawaz. "Detecting outliers on UCI repository datasets by Adaptive Rough Fuzzy clustering method." In 2016 Online International Conference on Green Engineering and Technologies (IC-GET). IEEE, 2016. http://dx.doi.org/10.1109/get.2016.7916697.

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Oliveira, Manoel, Felipe Muniz, and Ruann Farrapo. "Application for breast cancer classification using Computational Intelligence techniques." In Escola Regional de Computação Ceará, Maranhão, Piauí. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/ercemapi.2020.11473.

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In this work, a comparative study was carried out between two classification methods: The Multi layer Perceptron Artificial Neural Network (MLP ANN) and the method of classification of the Nearest Neighbors, used in the classification of the diagnosis of breast cancer. The data used in this work were taken from the UCI Machine Learning Repository and contains numerical data extracted from mammography images.In addition, the results were evaluated based on the cross-validation strategy.
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Sharma, Deepanshu, and Siddhartha Chauhan. "Heart Disease Prediction using Data Mining Classification Algorithms." In 8th International Conference on Computer Science and Information Technology. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.141511.

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Heart diseases, also referred to as "cardiovascular diseases," are a group of disorders that affect the heart. This illness can cause a heart attack, stroke, and other symptoms. After examining a few research papers on the subject, it became clear that the majority of them used a single machine learning algorithm to predict heart disease. A few of them state that they are unable to enhance their model's performance through optimization techniques. As a result of these findings, they encountered some difficulties in effectively predicting heart disease using their suggested method. In an earlie
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Jik Lee, Byoung. "Extracting the Significant Degrees of Attributes in Unlabeled Data using Unsupervised Machine Learning." In 4th International Conference on Computer Science and Information Technology (COMIT 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101608.

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We propose a valid approach to find the degree of important attributes in unlabeled dataset to improve the clustering performance. The significant degrees of attributes are extracted through the training of unsupervised simple competitive learning with the raw unlabeled data. These significant degrees are applied to the original dataset and generate the weighted dataset reflected by the degrees of influentialvalues for the set ofattributes. This work is simulated on the UCI Machine Learning repository dataset. The Scikit-learn K-Means clustering with raw data, scaled data, and the weighted dat
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Wang, Nan, Xibin Zhao, Yu Jiang, and Yue Gao. "Iterative Metric Learning for Imbalance Data Classification." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/389.

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In many classification applications, the amount of data from different categories usually vary significantly, such as software defect predication and medical diagnosis. Under such circumstances, it is essential to propose a proper method to solve the imbalance issue among the data. However, most of the existing methods mainly focus on improving the performance of classifiers rather than searching for an appropriate way to find an effective data space for classification. In this paper, we propose a method named Iterative Metric Learning (IML) to explore the correlations among imbalance data and
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Sakai, Hiroshi, Chenxi Liu, and Michinori Nakata. "Information Dilution: Granule-Based Information Hiding in Table Data - A Case of Lenses Data Set in UCI Machine Learning Repository." In 2016 Third International Conference on Computing Measurement Control and Sensor Network (CMCSN). IEEE, 2016. http://dx.doi.org/10.1109/cmcsn.2016.28.

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Sen, Anupam. "Data Mining and Principal Component Analysis on Coimbra Breast Cancer Dataset." In Intelligent Computing and Technologies Conference. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.115.5.

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Machine Learning (ML) techniques play an important role in the medical field. Early diagnosis is required to improve the treatment of carcinoma. During this analysis Breast Cancer Coimbra dataset (BCCD) with ten predictors are analyzed to classify carcinoma. In this paper method for feature selection and Machine learning algorithms are applied to the dataset from the UCI repository. WEKA (“Waikato Environment for Knowledge Analysis”) tool is used for machine learning techniques. In this paper Principal Component Analysis (PCA) is used for feature extraction. Different Machine Learning classifi
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Khan, Mohammad Mahmudur Rahman, Rezoana Bente Arif, Md Abu Bakr Siddique, and Mahjabin Rahman Oishe. "Study and Observation of the Variation of Accuracies of KNN, SVM, LMNN, ENN Algorithms on Eleven Different Datasets from UCI Machine Learning Repository." In 2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT). IEEE, 2018. http://dx.doi.org/10.1109/ceeict.2018.8628041.

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Costa e Silva, Fernanda, and Danton Diego Ferreira. "Classificação de nódulos mamários com máquina de vetores de suporte." In Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1601.

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O câncer de mama é o tipo mais comum entre mulheres e seu diagnóstico tardio é uma das causas do alto índice de mortalidade associado a esta doença. Para ajudar na interpretação dos exames para o diagnóstico correto, métodos de classificação têm sido utilizados para classificar nódulos em malignos ou benignos. Este trabalho retrata a utilização da Máquina de Vetores de Suporte (SVM), com diferentes funções Kernel, para classificação do conjunto de dados de resultados de exames para detecção de câncer de mama, realizando a separação das amostras em benignas e malignas, com implementação em Pyth
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Kaur, Simarjeet, Meenakshi Bansal, and Ashok Kumar Bathla. "A Comparitive Study of E-Mail Spam Detection using Various Machine Learning Techniques." In International Conference on Women Researchers in Electronics and Computing. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.114.56.

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Due to the rise in the use of messaging and mailing services, spam detection tasks are of much greater importance than before. In such a set of communications, efficient classification is a comparatively onerous job. For an addressee or any email that the user does not want to have in his inbox, spam can be defined as redundant or trash email. After pre-processing and feature extraction, various machine learning algorithms were applied to a Spam base dataset from the UCI Machine Learning repository in order to classify incoming emails into two categories: spam and non-spam. The outcomes of var
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