Literatura académica sobre el tema "UCI MACHINE LEARNING"
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Artículos de revistas sobre el tema "UCI MACHINE LEARNING"
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Aprianto, Kasiful. "Heart Disease UCI Machine Learning". JITCE (Journal of Information Technology and Computer Engineering) 5, n.º 02 (30 de septiembre de 2021): 70–74. http://dx.doi.org/10.25077/jitce.5.02.70-74.2021.
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Mohammad, Ahmad Saeed, Musab T. S. Al-Kaltakchi, Jabir Alshehabi Al-Ani y Jonathon A. Chambers. "Comprehensive Evaluations of Student Performance Estimation via Machine Learning". Mathematics 11, n.º 14 (18 de julio de 2023): 3153. http://dx.doi.org/10.3390/math11143153.
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Success in student learning is the primary aim of the educational system. Artificial intelligence utilizes data and machine learning to achieve excellence in student learning. In this paper, we exploit several machine learning techniques to estimate early student performance. Two main simulations are used for the evaluation. The first simulation used the Traditional Machine Learning Classifiers (TMLCs) applied to the House dataset, and they are Gaussian Naïve Bayes (GNB), Support Vector Machine (SVM), Decision Tree (DT), Multi-Layer Perceptron (MLP), Random Forest (RF), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA). The best results were achieved with the MLP classifier with a division of 80% training and 20% testing, with an accuracy of 88.89%. The fusion of these seven classifiers was also applied and the highest result was equal to the MLP. Moreover, in the second simulation, the Convolutional Neural Network (CNN) was utilized and evaluated on five main datasets, namely, House, Western Ontario University (WOU), Experience Application Programming Interface (XAPI), University of California-Irvine (UCI), and Analytics Vidhya (AV). The UCI dataset was subdivided into three datasets, namely, UCI-Math, UCI-Por, and UCI-Fused. Moreover, the AV dataset has three targets which are Math, Reading, and Writing. The best accuracy results were achieved at 97.5%, 99.55%, 98.57%, 99.28%, 99.40%, 99.67%, 92.93%, 96.99%, and 96.84% for the House, WOU, XAPI, UCI-Math, UCI-Por, UCI-Fused, AV-Math, AV-Reading, and AV-Writing datasets, respectively, under the same protocol of evaluation. The system demonstrates that the proposed CNN-based method surpasses all seven conventional methods and other state-of-the-art-work.
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TURAN, SELIN CEREN y MEHMET ALI CENGIZ. "ENSEMBLE LEARNING ALGORITHMS". Journal of Science and Arts 22, n.º 2 (30 de junio de 2022): 459–70. http://dx.doi.org/10.46939/j.sci.arts-22.2-a18.
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Artificial intelligence is a method that is increasingly becoming widespread in all areas of life and enables machines to imitate human behavior. Machine learning is a subset of artificial intelligence techniques that use statistical methods to enable machines to evolve with experience. As a result of the advancement of technology and developments in the world of science, the interest and need for machine learning is increasing day by day. Human beings use machine learning techniques in their daily life without realizing it. In this study, ensemble learning algorithms, one of the machine learning techniques, are mentioned. The methods used in this study are Bagging and Adaboost algorithms which are from Ensemble Learning Algorithms. The main purpose of this study is to find the best performing classifier with the Classification and Regression Trees (CART) basic classifier on three different data sets taken from the UCI machine learning database and then to obtain the ensemble learning algorithms that can make this performance better and more determined using two different ensemble learning algorithms. For this purpose, the performance measures of the single basic classifier and the ensemble learning algorithms were compared
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Vranjković, Vuk S., Rastislav J. R. Struharik y Ladislav A. Novak. "Reconfigurable Hardware for Machine Learning Applications". Journal of Circuits, Systems and Computers 24, n.º 05 (8 de abril de 2015): 1550064. http://dx.doi.org/10.1142/s0218126615500644.
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This paper proposes universal coarse-grained reconfigurable computing architecture for hardware implementation of decision trees (DTs), artificial neural networks (ANNs), and support vector machines (SVMs), suitable for both field programmable gate arrays (FPGA) and application specific integrated circuits (ASICs) implementation. Using this universal architecture, two versions of DTs (functional DT and axis-parallel DT), two versions of SVMs (with polynomial and radial kernel) and two versions of ANNs (multi layer perceptron ANN and radial basis ANN) machine learning classifiers, have been implemented in FPGA. Experimental results, based on 18 benchmark datasets of standard UCI machine learning repository database, show that FPGA implementation provides significant improvement (1–2 orders of magnitude) in the average instance classification time, in comparison with software implementations based on R project.
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Kibria, Md Golam y Mehmet Sevkli. "Application of Deep Learning for Credit Card Approval: A Comparison with Two Machine Learning Techniques". International Journal of Machine Learning and Computing 11, n.º 4 (agosto de 2021): 286–90. http://dx.doi.org/10.18178/ijmlc.2021.11.4.1049.
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The increased credit card defaulters have forced the companies to think carefully before the approval of credit applications. Credit card companies usually use their judgment to determine whether a credit card should be issued to the customer satisfying certain criteria. Some machine learning algorithms have also been used to support the decision. The main objective of this paper is to build a deep learning model based on the UCI (University of California, Irvine) data sets, which can support the credit card approval decision. Secondly, the performance of the built model is compared with the other two traditional machine learning algorithms: logistic regression (LR) and support vector machine (SVM). Our results show that the overall performance of our deep learning model is slightly better than that of the other two models.
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Anderies, Anderies, Jalaludin Ar Raniry William Tchin, Prambudi Herbowo Putro, Yudha Putra Darmawan y Alexander Agung Santoso Gunawan. "Prediction of Heart Disease UCI Dataset Using Machine Learning Algorithms". Engineering, MAthematics and Computer Science (EMACS) Journal 4, n.º 3 (30 de septiembre de 2022): 87–93. http://dx.doi.org/10.21512/emacsjournal.v4i3.8683.
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Heart disease is inflammation or damage to the heart and blood vessels over time. the disease can affect anyone of any age, gender, or social status. After many studies trying to overcome and learn about heart disease, in the end, this disease can be detected using machine learning systems. It predicts the likelihood of developing heart disease. The results of this system give the probability of heart disease as a percentage. Data collection using secret data mining. The data assets handled in python programming use two main algorithms for machine learning, the decision tree algorithm, and the Bayes naive algorithm which shows the best of both for heart disease accuracy. The results we get from this study show that the SVM algorithm is the algorithm with the most excellent precision. and the highest accuracy with a score of 85% in predicting heart disease using machine learning algorithms.Heart disease is inflammation or damage to the heart and blood vessels over time. the disease can affect anyone of any age, gender, or social status. After many studies trying to overcome and learn about heart disease, in the end, this disease can be detected using machine learning systems. It predicts the likelihood of developing heart disease. The results of this system give the probability of heart disease as a percentage. Data collection using secret data mining. The data assets handled in python programming use two main algorithms for machine learning, the decision tree algorithm, and the Bayes naive algorithm which shows the best of both for heart disease accuracy. The results we get from this study show that the SVM algorithm is the algorithm with the most excellent precision. and the highest accuracy with a score of 85% in predicting heart disease using machine learning algorithms.
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Verma, Raunak, Shashank Tandon y Mr Vinayak. "Heart Disease Prediction using Machine Learning". International Journal for Research in Applied Science and Engineering Technology 10, n.º 5 (31 de mayo de 2022): 1872–76. http://dx.doi.org/10.22214/ijraset.2022.42687.
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Abstract: The term "heart disease" refers to any heart disease or condition that can cause heart problems. Cardiovascular disease (CVD) is the leading cause of death worldwide, taking many lives each year. CVD is a group of cardiovascular diseases and includes heart disease, cerebrovascular disease, rheumatic heart disease and other conditions. According to the World Health Organization (WHO), more than 17.9 million people worldwide die each year from coronary heart disease. If we take the example of India, every year the number of deaths due to heart disease has increased. Studies show that, from 2014 to 2019 the number of deaths from heart disease increased by 53%. Many threatening factors such as personal and work habits and genetic predisposition are major causes of heart disease. A variety of harmful habits such as smoking, alcohol and caffeine overdose, stress, and inactivity as well as other physical factors such as obesity, high blood pressure, high blood cholesterol, and pre-existing heart conditions are the main causes of heart disease. Over time, these harmful substances cause changes in the heart and blood vessels that can lead to heart attacks and strokes. Therefore, prevention of heart disease is very important to prevent these dangerous events and other potential complications of heart disease. Machine learning is a flexible part of AI that helps predict heart disease. In this research work, we will use the UCI database with 14 attributes to predict heart disease. The main goal of this study is to use ML algorithms to improve the heart disease prediction system and to more accurately predict these diseases in patients, thereby reducing the number of deaths by alerting patients. Keywords: Heart Diseases, Classification Algorithms, Machine Learning, UCI dataset.
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Hamed, Samer, Abdelwadood Mesleh y Abdullah Arabiyyat. "Breast Cancer Detection Using Machine Learning Algorithms". International Journal of Computer Science and Mobile Computing 10, n.º 11 (30 de noviembre de 2021): 4–11. http://dx.doi.org/10.47760/ijcsmc.2021.v10i11.002.
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This paper presents a computer-aided design (CAD) system that detects breast cancers (BCs). BC detection uses random forest, AdaBoost, logistic regression, decision trees, naïve Bayes and conventional neural networks (CNNs) classifiers, these machine learning (ML) based algorithms are trained to predicting BCs (malignant or benign) on BC Wisconsin data-set from the UCI repository, in which attribute clump thickness is used as evaluation class. The effectiveness of these ML algorithms are evaluated in terms of accuracy and F-measure; random forest outperformed the other classifiers and achieved 99% accuracy and 99% F-measure.
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Alnemari, Shouq y Majid Alshammari. "Detecting Phishing Domains Using Machine Learning". Applied Sciences 13, n.º 8 (7 de abril de 2023): 4649. http://dx.doi.org/10.3390/app13084649.
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Phishing is an online threat where an attacker impersonates an authentic and trustworthy organization to obtain sensitive information from a victim. One example of such is trolling, which has long been considered a problem. However, recent advances in phishing detection, such as machine learning-based methods, have assisted in combatting these attacks. Therefore, this paper develops and compares four models for investigating the efficiency of using machine learning to detect phishing domains. It also compares the most accurate model of the four with existing solutions in the literature. These models were developed using artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and random forest (RF) techniques. Moreover, the uniform resource locator’s (URL’s) UCI phishing domains dataset is used as a benchmark to evaluate the models. Our findings show that the model based on the random forest technique is the most accurate of the other four techniques and outperforms other solutions in the literature.
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Suneetha Rani R, Gayathri B, Venkata Surya M, Jharani Asha Kiran P y Siva Krishna R. "Detecting counterfeit banknotes with machine learning". South Asian Journal of Engineering and Technology 12, n.º 3 (11 de julio de 2022): 146–51. http://dx.doi.org/10.26524/sajet.2022.12.40.
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The one important asset of our country is Bank currency and to create discrepancies of money miscreants introduce the fake notes which resembles to original note in thefinancial market. During demonetization time it is seen that so much of fake currency is floating in market. In general, by a human being, it is very difficult to identify forged note from the genuine not instead of various parameters designed for identification as many features of forged note are similar to original one. To discriminate between fake bank currency and original note is a challenging task. So, there must be an automated system that will be available in banks or in ATM machines. To design such an automated system there is need to design an efficient algorithm which is able to predict weather the banknote is genuine or forged bank currency as fake notes are designed with high precision. In this project six supervised machine learning algorithms are applied on dataset available on UCI machine learning repository for detection of Bank currency authentication
Tesis sobre el tema "UCI MACHINE LEARNING"
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Modi, Navikkumar. "Machine Learning and Statistical Decision Making for Green Radio". Thesis, CentraleSupélec, 2017. http://www.theses.fr/2017SUPL0002/document.
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Cette thèse étudie les techniques de gestion intelligente du spectre et de topologie des réseaux via une approche radio intelligente dans le but d’améliorer leur capacité, leur qualité de service (QoS – Quality of Service) et leur consommation énergétique. Les techniques d’apprentissage par renforcement y sont utilisées dans le but d’améliorer les performances d’un système radio intelligent. Dans ce manuscrit, nous traitons du problème d’accès opportuniste au spectre dans le cas de réseaux intelligents sans infrastructure. Nous nous plaçons dans le cas où aucune information n’est échangée entre les utilisateurs secondaires (pour éviter les surcoûts en transmissions). Ce problème particulier est modélisé par une approche dite de bandits manchots « restless » markoviens multi-utilisateurs (multi-user restless Markov MAB -multi¬armed bandit). La contribution principale de cette thèse propose une stratégie d’apprentissage multi-joueurs qui prend en compte non seulement le critère de disponibilité des canaux (comme déjà étudié dans la littérature et une thèse précédente au laboratoire), mais aussi une métrique de qualité, comme par exemple le niveau d’interférence mesuré (sensing) dans un canal (perturbations issues des canaux adjacents ou de signaux distants). Nous prouvons que notre stratégie, RQoS-UCB distribuée (distributed restless QoS-UCB – Upper Confidence Bound), est quasi optimale car on obtient des performances au moins d’ordre logarithmique sur son regret. En outre, nous montrons par des simulations que les performances du système intelligent proposé sont améliorées significativement par l’utilisation de la solution d’apprentissage proposée permettant à l’utilisateur secondaire d’identifier plus efficacement les ressources fréquentielles les plus disponibles et de meilleure qualité. Cette thèse propose également un nouveau modèle d’apprentissage par renforcement combiné à un transfert de connaissance afin d’améliorer l’efficacité énergétique (EE) des réseaux cellulaires hétérogènes. Nous formulons et résolvons un problème de maximisation de l’EE pour le cas de stations de base (BS – Base Stations) dynamiquement éteintes et allumées (ON-OFF). Ce problème d’optimisation combinatoire peut aussi être modélisé par des bandits manchots « restless » markoviens. Par ailleurs, une gestion dynamique de la topologie des réseaux hétérogènes, utilisant l’algorithme RQoS-UCB, est proposée pour contrôler intelligemment le mode de fonctionnement ON-OFF des BS, dans un contexte de trafic et d’étude de capacité multi-cellulaires. Enfin une méthode incluant le transfert de connaissance « transfer RQoS-UCB » est proposée et validée par des simulations, pour pallier les pertes de récompense initiales et accélérer le processus d’apprentissage, grâce à la connaissance acquise à d’autres périodes temporelles correspondantes à la période courante (même heure de la journée la veille, ou même jour de la semaine par exemple). La solution proposée de gestion dynamique du mode ON-OFF des BS permet de diminuer le nombre de BS actives tout en garantissant une QoS adéquate en atténuant les fluctuations de la QoS lors des variations du trafic et en améliorant les conditions au démarrage de l’apprentissage. Ainsi, l’efficacité énergétique est grandement améliorée. Enfin des démonstrateurs en conditions radio réelles ont été développés pour valider les solutions d’apprentissage étudiées. Les algorithmes ont également été confrontés à des bases de données de mesures effectuées par un partenaire dans la gamme de fréquence HF, pour des liaisons transhorizon. Les résultats confirment la pertinence des solutions d’apprentissage proposées, aussi bien en termes d’optimisation de l’utilisation du spectre fréquentiel, qu’en termes d’efficacité énergétiqueFuture cellular network technologies are targeted at delivering self-organizable and ultra-high capacity networks, while reducing their energy consumption. This thesis studies intelligent spectrum and topology management through cognitive radio techniques to improve the capacity density and Quality of Service (QoS) as well as to reduce the cooperation overhead and energy consumption. This thesis investigates how reinforcement learning can be used to improve the performance of a cognitive radio system. In this dissertation, we deal with the problem of opportunistic spectrum access in infrastructureless cognitive networks. We assume that there is no information exchange between users, and they have no knowledge of channel statistics and other user's actions. This particular problem is designed as multi-user restless Markov multi-armed bandit framework, in which multiple users collect a priori unknown reward by selecting a channel. The main contribution of the dissertation is to propose a learning policy for distributed users, that takes into account not only the availability criterion of a band but also a quality metric linked to the interference power from the neighboring cells experienced on the sensed band. We also prove that the policy, named distributed restless QoS-UCB (RQoS-UCB), achieves at most logarithmic order regret. Moreover, numerical studies show that the performance of the cognitive radio system can be significantly enhanced by utilizing proposed learning policies since the cognitive devices are able to identify the appropriate resources more efficiently. This dissertation also introduces a reinforcement learning and transfer learning frameworks to improve the energy efficiency (EE) of the heterogeneous cellular network. Specifically, we formulate and solve an energy efficiency maximization problem pertaining to dynamic base stations (BS) switching operation, which is identified as a combinatorial learning problem, with restless Markov multi-armed bandit framework. Furthermore, a dynamic topology management using the previously defined algorithm, RQoS-UCB, is introduced to intelligently control the working modes of BSs, based on traffic load and capacity in multiple cells. Moreover, to cope with initial reward loss and to speed up the learning process, a transfer RQoS-UCB policy, which benefits from the transferred knowledge observed in historical periods, is proposed and provably converges. Then, proposed dynamic BS switching operation is demonstrated to reduce the number of activated BSs while maintaining an adequate QoS. Extensive numerical simulations demonstrate that the transfer learning significantly reduces the QoS fluctuation during traffic variation, and it also contributes to a performance jump-start and presents significant EE improvement under various practical traffic load profiles. Finally, a proof-of-concept is developed to verify the performance of proposed learning policies on a real radio environment and real measurement database of HF band. Results show that proposed multi-armed bandit learning policies using dual criterion (e.g. availability and quality) optimization for opportunistic spectrum access is not only superior in terms of spectrum utilization but also energy efficient
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Duncan, Andrew Paul. "The analysis and application of artificial neural networks for early warning systems in hydrology and the environment". Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/17569.
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Artificial Neural Networks (ANNs) have been comprehensively researched, both from a computer scientific perspective and with regard to their use for predictive modelling in a wide variety of applications including hydrology and the environment. Yet their adoption for live, real-time systems remains on the whole sporadic and experimental. A plausible hypothesis is that this may be at least in part due to their treatment heretofore as “black boxes” that implicitly contain something that is unknown, or even unknowable. It is understandable that many of those responsible for delivering Early Warning Systems (EWS) might not wish to take the risk of implementing solutions perceived as containing unknown elements, despite the computational advantages that ANNs offer. This thesis therefore builds on existing efforts to open the box and develop tools and techniques that visualise, analyse and use ANN weights and biases especially from the viewpoint of neural pathways from inputs to outputs of feedforward networks. In so doing, it aims to demonstrate novel approaches to self-improving predictive model construction for both regression and classification problems. This includes Neural Pathway Strength Feature Selection (NPSFS), which uses ensembles of ANNs trained on differing subsets of data and analysis of the learnt weights to infer degrees of relevance of the input features and so build simplified models with reduced input feature sets. Case studies are carried out for prediction of flooding at multiple nodes in urban drainage networks located in three urban catchments in the UK, which demonstrate rapid, accurate prediction of flooding both for regression and classification. Predictive skill is shown to reduce beyond the time of concentration of each sewer node, when actual rainfall is used as input to the models. Further case studies model and predict statutory bacteria count exceedances for bathing water quality compliance at 5 beaches in Southwest England. An illustrative case study using a forest fires dataset from the UCI machine learning repository is also included. Results from these model ensembles generally exhibit improved performance, when compared with single ANN models. Also ensembles with reduced input feature sets, using NPSFS, demonstrate as good or improved performance when compared with the full feature set models. Conclusions are drawn about a new set of tools and techniques, including NPSFS and visualisation techniques for inspection of ANN weights, the adoption of which it is hoped may lead to improved confidence in the use of ANN for live real-time EWS applications.
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Bouneffouf, Djallel. "DRARS, A Dynamic Risk-Aware Recommender System". Phd thesis, Institut National des Télécommunications, 2013. http://tel.archives-ouvertes.fr/tel-01026136.
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L'immense quantité d'information générée et gérée au quotidien par les systèmes d'information et leurs utilisateurs conduit inéluctablement ?a la problématique de surcharge d'information. Dans ce contexte, les systèmes de recommandation traditionnels fournissent des informations pertinentes aux utilisateurs. Néanmoins, avec la propagation récente des dispositifs mobiles (Smartphones et tablettes), nous constatons une migration progressive des utilisateurs vers la manipulation d'environnements pérvasifs. Le problème avec les approches traditionnelles de recommandation est qu'elles n'utilisent pas toute l'information disponible pour produire des recommandations. Davantage d'informations contextuelles pourraient être utilisées dans le processus de recommandation pour aboutir à des recommandations plus précises. Les systèmes de recommandations sensibles au contexte (CARS) combinent les caractéristiques des systèmes sensibles au contexte et des systèmes de recommandation an de fournir des informations personnalisées aux utilisateurs dans des environnements ubiquitaires. Dans cette perspective ou tout ce qui concerne l'utilisateur est dynamique, les contenus qu'il manipule et son environnement, deux questions principales doivent être adressées : i) Comment prendre en compte la dynamicité des contenus de l'utilisateur ? et ii ) Comment éviter d'être intrusif en particulier dans des situations critiques ?. En réponse ?a ces questions, nous avons développé un système de recommandation dynamique et sensible au risque appelé DRARS (Dynamic Risk-Aware Recommender System), qui modélise la recommandation sensible au contexte comme un problème de bandit. Ce système combine une technique de filtrage basée sur le contenu et un algorithme de bandit contextuel. Nous avons montré que DRARS améliore la stratégie de l'algorithme UCB (Upper Con dence Bound), le meilleur algorithme actuellement disponible, en calculant la valeur d'exploration la plus optimale pour maintenir un compromis entre exploration et exploitation basé sur le niveau de risque de la situation courante de l'utilisateur. Nous avons mené des expériences dans un contexte industriel avec des données réelles et des utilisateurs réels et nous avons montré que la prise en compte du niveau de risque de la situation de l'utilisateur augmentait significativement la performance du système de recommandation.
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Fanciulli, Matteo. "Forecast sull'impatto della crescita esponenziale della tecnologia nel mondo del lavoro e nella società". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016.
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In questa tesi esaminerò alcuni aspetti fondamentali della tecnologia moderna tra cui alcune leggi chiave che spiegheranno come mai la crescente disoccupazione in Europa, e in occidente in generale, non è causata solamente da crisi finanziarie o politiche, ma dall'intrinseca natura della tecnologia stessa. Ci troveremo nella situazione nella quale una persona non sia in grado di trovare un'occupazione non a causa di demeriti propri, ma poiché il sistema è diventato talmente ottimizzato da tagliare completamente la necessità di alcuni ruoli chiave nel sistema di lavoro. Spiegherò quali sono le strategie da attuare per evitare di trovarsi in questo nuovo sistema di occupazione senza un ruolo al suo interno, quali sono le politiche che un governo debba attuare per garantire i necessari bisogni primari dei propri cittadini, le strutture che ogni azienda deve creare per rimanere all'interno del proprio settore di investimento.
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van, Merriënboer Bart. "Sequence-to-sequence learning for machine translation and automatic differentiation for machine learning software tools". Thèse, 2018. http://hdl.handle.net/1866/21743.
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Askari, Hemmat Reyhane. "SLA violation prediction : a machine learning perspective". Thèse, 2016. http://hdl.handle.net/1866/18754.
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Mokaddem, Mouna. "Learning a graph made of boolean function nodes : a new approach in machine learning". Thèse, 2016. http://hdl.handle.net/1866/18763.
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Dans ce document, nous présentons une nouvelle approche en apprentissage machine
pour la classification. Le cadre que nous proposons est basé sur des circuits booléens,
plus précisément le classifieur produit par notre algorithme a cette forme. L’utilisation
des bits et des portes logiques permet à l’algorithme d’apprentissage et au classifieur
d’utiliser des opérations vectorielles binaires très efficaces. La qualité du classifieur, produit
par notre approche, se compare très favorablement à ceux qui sont produits par des
techniques classiques, à la fois en termes d’efficacité et de précision. En outre, notre
approche peut être utilisée dans un contexte où la confidentialité est une nécessité, par
exemple, nous pouvons classer des données privées. Ceci est possible car le calcul ne
peut être effectué que par des circuits booléens et les données chiffrées sont quantifiées
en bits. De plus, en supposant que le classifieur a été déjà entraîné, il peut être alors
facilement implémenté sur un FPGA car ces circuits sont également basés sur des portes
logiques et des opérations binaires. Par conséquent, notre modèle peut être facilement
intégré dans des systèmes de classification en temps réel.In this document we present a novel approach in machine learning for classification. The framework we propose is based on boolean circuits, more specifically the classifier produced by our algorithm has that form. Using bits and boolean gates enable the learning algorithm and the classifier to use very efficient boolean vector operations. The accuracy of the classifier we obtain with our framework compares very favourably with those produced by conventional techniques, both in terms of efficiency and accuracy. Furthermore, the framework can be used in a context where information privacy is a necessity, for example we can classify private data. This can be done because computation can be performed only through boolean circuits as encrypted data is quantized in bits. Moreover, assuming that the classifier was trained, it can then be easily implemented on FPGAs (i.e., Field-programmable gate array) as those circuits are also based on logic gates and bitwise operations. Therefore, our model can be easily integrated in real-time classification systems.
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Chapados, Nicolas. "Sequential Machine learning Approaches for Portfolio Management". Thèse, 2009. http://hdl.handle.net/1866/3578.
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Cette thèse envisage un ensemble de méthodes permettant aux algorithmes d'apprentissage statistique de mieux traiter la nature séquentielle des problèmes de gestion de portefeuilles financiers.
Nous débutons par une considération du problème général de la composition d'algorithmes d'apprentissage devant gérer des tâches séquentielles, en particulier celui de la mise-à-jour efficace des ensembles d'apprentissage dans un cadre de validation séquentielle. Nous énumérons les desiderata que des primitives de composition doivent satisfaire, et faisons ressortir la difficulté de les atteindre de façon rigoureuse et efficace. Nous poursuivons en présentant un ensemble d'algorithmes qui atteignent ces objectifs et présentons une étude de cas d'un système complexe de prise de décision financière utilisant ces techniques.
Nous décrivons ensuite une méthode générale permettant de transformer un problème de décision séquentielle non-Markovien en un problème d'apprentissage supervisé en employant un algorithme de recherche basé sur les K meilleurs chemins. Nous traitons d'une application en gestion de portefeuille où nous entraînons un algorithme d'apprentissage à optimiser directement un ratio de Sharpe (ou autre critère non-additif incorporant une aversion au risque). Nous illustrons l'approche par une étude expérimentale approfondie, proposant une architecture de réseaux de neurones spécialisée à la gestion de portefeuille et la comparant à plusieurs alternatives.
Finalement, nous introduisons une représentation fonctionnelle de séries chronologiques permettant à des prévisions d'être effectuées sur un horizon variable, tout en utilisant un ensemble informationnel révélé de manière progressive. L'approche est basée sur l'utilisation des processus Gaussiens, lesquels fournissent une matrice de covariance complète entre tous les points pour lesquels une prévision est demandée. Cette information est utilisée à bon escient par un algorithme qui transige activement des écarts de cours (price spreads) entre des contrats à terme sur commodités. L'approche proposée produit, hors échantillon, un rendement ajusté pour le risque significatif, après frais de transactions, sur un portefeuille de 30 actifs.This thesis considers a number of approaches to make machine learning algorithms better suited to the sequential nature of financial portfolio management tasks. We start by considering the problem of the general composition of learning algorithms that must handle temporal learning tasks, in particular that of creating and efficiently updating the training sets in a sequential simulation framework. We enumerate the desiderata that composition primitives should satisfy, and underscore the difficulty of rigorously and efficiently reaching them. We follow by introducing a set of algorithms that accomplish the desired objectives, presenting a case-study of a real-world complex learning system for financial decision-making that uses those techniques. We then describe a general method to transform a non-Markovian sequential decision problem into a supervised learning problem using a K-best paths search algorithm. We consider an application in financial portfolio management where we train a learning algorithm to directly optimize a Sharpe Ratio (or other risk-averse non-additive) utility function. We illustrate the approach by demonstrating extensive experimental results using a neural network architecture specialized for portfolio management and compare against well-known alternatives. Finally, we introduce a functional representation of time series which allows forecasts to be performed over an unspecified horizon with progressively-revealed information sets. By virtue of using Gaussian processes, a complete covariance matrix between forecasts at several time-steps is available. This information is put to use in an application to actively trade price spreads between commodity futures contracts. The approach delivers impressive out-of-sample risk-adjusted returns after transaction costs on a portfolio of 30 spreads.
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Gidel, Gauthier. "Multi-player games in the era of machine learning". Thesis, 2020. http://hdl.handle.net/1866/24800.
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Parmi tous les jeux de société joués par les humains au cours de l’histoire, le jeu de go était considéré comme l’un des plus difficiles à maîtriser par un programme informatique [Van Den Herik et al., 2002]; Jusqu’à ce que ce ne soit plus le cas [Silveret al., 2016]. Cette percée révolutionnaire [Müller, 2002, Van Den Herik et al., 2002] fût le fruit d’une combinaison sophistiquée de Recherche arborescente Monte-Carlo et de techniques d’apprentissage automatique pour évaluer les positions du jeu, mettant en lumière le grand potentiel de l’apprentissage automatique pour résoudre des jeux. L’apprentissage antagoniste, un cas particulier de l’optimisation multiobjective, est un outil de plus en plus utile dans l’apprentissage automatique. Par exemple, les jeux à deux joueurs et à somme nulle sont importants dans le domain des réseaux génératifs antagonistes [Goodfellow et al., 2014] ainsi que pour maîtriser des jeux comme le Go ou le Poker en s’entraînant contre lui-même [Silver et al., 2017, Brown andSandholm, 2017]. Un résultat classique de la théorie des jeux indique que les jeux convexes-concaves ont toujours un équilibre [Neumann, 1928]. Étonnamment, les praticiens en apprentissage automatique entrainent avec succès une seule paire de réseaux de neurones dont l’objectif est un problème de minimax non-convexe et non-concave alors que pour une telle fonction de gain, l’existence d’un équilibre de Nash n’est pas garantie en général. Ce travail est une tentative d'établir une solide base théorique pour l’apprentissage dans les jeux. La première contribution explore le théorème minimax pour une classe particulière de jeux non-convexes et non-concaves qui englobe les réseaux génératifs antagonistes. Cette classe correspond à un ensemble de jeux à deux joueurs et a somme nulle joués avec des réseaux de neurones. Les deuxième et troisième contributions étudient l’optimisation des problèmes minimax, et plus généralement, les inégalités variationnelles dans le cadre de l’apprentissage automatique. Bien que la méthode standard de descente de gradient ne parvienne pas à converger vers l’équilibre de Nash de jeux convexes-concaves simples, il existe des moyens d’utiliser des gradients pour obtenir des méthodes qui convergent. Nous étudierons plusieurs techniques telles que l’extrapolation, la moyenne et la quantité de mouvement à paramètre négatif. La quatrième contribution fournit une étude empirique du comportement pratique des réseaux génératifs antagonistes. Dans les deuxième et troisième contributions, nous diagnostiquons que la méthode du gradient échoue lorsque le champ de vecteur du jeu est fortement rotatif. Cependant, une telle situation peut décrire un pire des cas qui ne se produit pas dans la pratique. Nous fournissons de nouveaux outils de visualisation afin d’évaluer si nous pouvons détecter des rotations dans comportement pratique des réseaux génératifs antagonistes.Among all the historical board games played by humans, the game of go was considered one of the most difficult to master by a computer program [Van Den Heriket al., 2002]; Until it was not [Silver et al., 2016]. This odds-breaking break-through [Müller, 2002, Van Den Herik et al., 2002] came from a sophisticated combination of Monte Carlo tree search and machine learning techniques to evaluate positions, shedding light upon the high potential of machine learning to solve games. Adversarial training, a special case of multiobjective optimization, is an increasingly useful tool in machine learning. For example, two-player zero-sum games are important for generative modeling (GANs) [Goodfellow et al., 2014] and mastering games like Go or Poker via self-play [Silver et al., 2017, Brown and Sandholm,2017]. A classic result in Game Theory states that convex-concave games always have an equilibrium [Neumann, 1928]. Surprisingly, machine learning practitioners successfully train a single pair of neural networks whose objective is a nonconvex-nonconcave minimax problem while for such a payoff function, the existence of a Nash equilibrium is not guaranteed in general. This work is an attempt to put learning in games on a firm theoretical foundation. The first contribution explores minimax theorems for a particular class of nonconvex-nonconcave games that encompasses generative adversarial networks. The proposed result is an approximate minimax theorem for two-player zero-sum games played with neural networks, including WGAN, StarCrat II, and Blotto game. Our findings rely on the fact that despite being nonconcave-nonconvex with respect to the neural networks parameters, the payoff of these games are concave-convex with respect to the actual functions (or distributions) parametrized by these neural networks. The second and third contributions study the optimization of minimax problems, and more generally, variational inequalities in the context of machine learning. While the standard gradient descent-ascent method fails to converge to the Nash equilibrium of simple convex-concave games, there exist ways to use gradients to obtain methods that converge. We investigate several techniques such as extrapolation, averaging and negative momentum. We explore these techniques experimentally by proposing a state-of-the-art (at the time of publication) optimizer for GANs called ExtraAdam. We also prove new convergence results for Extrapolation from the past, originally proposed by Popov [1980], as well as for gradient method with negative momentum. The fourth contribution provides an empirical study of the practical landscape of GANs. In the second and third contributions, we diagnose that the gradient method breaks when the game’s vector field is highly rotational. However, such a situation may describe a worst-case that does not occur in practice. We provide new visualization tools in order to exhibit rotations in practical GAN landscapes. In this contribution, we show empirically that the training of GANs exhibits significant rotations around Local Stable Stationary Points (LSSP), and we provide empirical evidence that GAN training converges to a stable stationary point, which is a saddle point for the generator loss, not a minimum, while still achieving excellent performance.
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Dauphin, Yann. "Advances in scaling deep learning algorithms". Thèse, 2015. http://hdl.handle.net/1866/13710.
Texto completoCapítulos de libros sobre el tema "UCI MACHINE LEARNING"
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Collaris, Dennis, Pratik Gajane, Joost Jorritsma, Jarke J. van Wijk y Mykola Pechenizkiy. "LEMON: Alternative Sampling for More Faithful Explanation Through Local Surrogate Models". En Advances in Intelligent Data Analysis XXI, 77–90. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30047-9_7.
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AbstractLocal surrogate learning is a popular and successful method for machine learning explanation. It uses synthetic transfer data to approximate a complex reference model. The sampling technique used for this transfer data has a significant impact on the provided explanation, but remains relatively unexplored in literature. In this work, we explore alternative sampling techniques in pursuit of more faithful and robust explanations, and present LEMON: a sampling technique that samples directly from the desired distribution instead of reweighting samples as done in other explanation techniques (e.g., LIME). Next, we evaluate our technique in a synthetic and UCI dataset-based experiment, and show that our sampling technique yields more faithful explanations compared to current state-of-the-art explainers.
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Abdalla, Hassan I. "A Brief Comparison of K-means and Agglomerative Hierarchical Clustering Algorithms on Small Datasets". En Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 623–32. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_64.
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AbstractIn this work, the agglomerative hierarchical clustering and K-means clustering algorithms are implemented on small datasets. Considering that the selection of the similarity measure is a vital factor in data clustering, two measures are used in this study - cosine similarity measure and Euclidean distance - along with two evaluation metrics - entropy and purity - to assess the clustering quality. The datasets used in this work are taken from UCI machine learning depository. The experimental results indicate that k-means clustering outperformed hierarchical clustering in terms of entropy and purity using cosine similarity measure. However, hierarchical clustering outperformed k-means clustering using Euclidean distance. It is noted that performance of clustering algorithm is highly dependent on the similarity measure. Moreover, as the number of clusters gets reasonably increased, the clustering algorithms’ performance gets higher.
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Lorenzi, Marco, Marie Deprez, Irene Balelli, Ana L. Aguila y Andre Altmann. "Integration of Multimodal Data". En Machine Learning for Brain Disorders, 573–97. New York, NY: Springer US, 2012. http://dx.doi.org/10.1007/978-1-0716-3195-9_19.
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AbstractThis chapter focuses on the joint modeling of heterogeneous information, such as imaging, clinical, and biological data. This kind of problem requires to generalize classical uni- and multivariate association models to account for complex data structure and interactions, as well as high data dimensionality.Typical approaches are essentially based on the identification of latent modes of maximal statistical association between different sets of features and ultimately allow to identify joint patterns of variations between different data modalities, as well as to predict a target modality conditioned on the available ones. This rationale can be extended to account for several data modalities jointly, to define multi-view, or multi-channel, representation of multiple modalities. This chapter covers both classical approaches such as partial least squares (PLS) and canonical correlation analysis (CCA), along with most recent advances based on multi-channel variational autoencoders. Specific attention is here devoted to the problem of interpretability and generalization of such high-dimensional models. These methods are illustrated in different medical imaging applications, and in the joint analysis of imaging and non-imaging information, such as -omics or clinical data.
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Xiao, Yuteng, Hongsheng Yin, Kaijian Xia, Yundong Zhang y Honggang Qi. "Utilization of CNN-LSTM Model in Prediction of Multivariate Time Series for UCG". En Machine Learning for Cyber Security, 429–40. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62463-7_40.
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Wang, Kai, Bryan Wilder, Sze-chuan Suen, Bistra Dilkina y Milind Tambe. "Improving GP-UCB Algorithm by Harnessing Decomposed Feedback". En Machine Learning and Knowledge Discovery in Databases, 555–69. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43823-4_44.
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Gong, Pixin, Xiaoran Huang, Chenyu Huang y Shiliang Wang. "Modeling on Outdoor Thermal Comfort in Traditional Residential Neighborhoods in Beijing Based on GAN". En Computational Design and Robotic Fabrication, 273–83. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8405-3_23.
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AbstractWith the support of new urban science and technology, the bottom-up and human-centered space quality research has become the key to delicacy urban governance, of which the Universal Thermal Climate Index (UTCI) have a severe influence. However, in the studies of actual UTCI, datasets are mostly obtained from on-site measurement data or simulation data, which is costly and ineffective. So, how to efficiently and rapidly conduct a large-scale and fine-grained outdoor environmental comfort evaluation based on the outdoor environment is the problem to be solved in this study. Compared to the conventional qualitative analysis methods, the rapidly developing algorithm-supported data acquisition and machine learning modelling are more efficient and accurate. Goodfellow proposed Generative Adversarial Nets (GANs) in 2014, which can successfully be applied to image generation with insufficient training data. In this paper, we propose an approach based on a generative adversarial network (GAN) to predict UTCI in traditional blocks. 36000 data samples were obtained from the simulations, to train a pix2pix model based on the TensorFlow framework. After more than 300 thousand iterations, the model gradually converges, where the loss of the function gradually decreases with the increase of the number of iterations. Overall, the model has been able to understand the overall semantic information behind the UTCI graphs to a high degree. Study in this paper deeply integrates the method of data augmentation based on GAN and machine learning modeling, which can be integrated into the workflow of detailed urban design and sustainable construction in the future.
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Shafi, Mujtaba, Amit Jain y Majid Zaman. "Applying Machine Learning Algorithms on Urban Heat Island (UHI) Dataset". En International Conference on Innovative Computing and Communications, 725–32. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3679-1_63.
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Dong, W., Y. Huang, B. Lehane y G. Ma. "An Intelligent Multi-objective Design Optimization Method for Nanographite-Based Electrically Conductive Cementitious Composites". En Lecture Notes in Civil Engineering, 339–46. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3330-3_35.
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AbstractNanographite (NG) is a promising conductive filler for producing effective electrically conductive cementitious composites for use in structural health monitoring methods. Since the acceptable mechanical strength and electrical resistivity are both required, the design of NG-based cementitious composite (NGCC) is a complicated multi-objective optimization problem. This study proposes a data-driven method to address this multi-objective design optimization (MODO) issue for NGCC using machine learning (ML) techniques and non-dominated sorting genetic algorithm (NSGA-II). Prediction models of the uniaxial compressive strength (UCS) and electrical resistivity (ER) of NGCC are established by Bayesian-tuned XGBoost with prepared datasets. Results show that they have excellent performance in predicting both properties with high R2 (0.95 and 0.92, 0.99 and 0.98) and low mean absolute error (1.24 and 3.44, 0.15 and 0.22). The influence of critical features on NGCC’s properties are quantified by ML theories, which help determine the variables to be optimized and define their constraints for the MODO. The MODO program is developed on the basis of NSGA-II. It optimizes NGCC’s properties of UCS and ER simultaneously, and successfully achieves a set of Pareto solutions, which can facilitate appropriate parameters selections for the NGCC design.
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Kochupillai, Mrinalini y Julia Köninger. "Creating a Digital Marketplace for Agrobiodiversity and Plant Genetic Sequence Data: Legal and Ethical Considerations of an AI and Blockchain Based Solution". En Towards Responsible Plant Data Linkage: Data Challenges for Agricultural Research and Development, 223–53. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13276-6_12.
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AbstractThe EU regulation on ‘Organic Production and Labelling of Organic Products’ opens the door for the creation of an EU-wide marketplace for agrobiodiversity contained in so-called “heterogeneous materials”. However, the creation of such a marketplace presupposes the existence of optimal demand and supply of agrobiodiversity, linked plant genetic sequence data and local/traditional knowledge on how best to use agrobiodiversity. Farmers’ tendency to prefer genetically uniform “high yielding” seeds and the adoption of chemical intensive farming have compromised the supply of agrobiodiversity. At the same time, regulatory regimes have disincentivized the use of agrobiodiversity in research and breeding programs, resulting in a lack of demand for agrobiodiversity. This chapter argues that these trends result from (inadvertent) inequities in existing regulatory frameworks that primarily support uni-directional data/knowledge flows from the formal sector (academia, industry) to the informal sector (farmers). We propose ways in which rapidly evolving technologies like blockchain/DLTs and AI/Machine Learning can (and should) diversify the direction of scientific research as well as of data/knowledge flows in the agricultural sector. The chapter thus provides food for thought for developing novel regulatory frameworks and ethical business models for robust digital marketplaces for agrobiodiversity for the benefit of farmers, researchers, and the environment.
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B., Shiva Shanta Mani y Manikandan V. M. "Heart Disease Prediction Using Machine Learning". En Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning, 373–81. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-2742-9.ch018.
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Heart disease is one of the most common and serious health issues in all the age groups. The food habits, mental stress, smoking, etc. are a few reasons for heart diseases. Diagnosing heart issues at an early stage is very much important to take proper treatment. The treatment of heart disease at the later stage is very expensive and risky. In this chapter, the authors discuss machine learning approaches to predict heart disease from a set of health parameters collected from a person. The heart disease dataset from the UCI machine learning repository is used for the study. This chapter discusses the heart disease prediction capability of four well-known machine learning approaches: naive Bayes classifier, KNN classifier, decision tree classifier, random forest classifier.
Actas de conferencias sobre el tema "UCI MACHINE LEARNING"
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Eklund, Peter W. "Comparative study of public-domain supervised machine-learning accuracy on the UCI database". En AeroSense '99, editado por Belur V. Dasarathy. SPIE, 1999. http://dx.doi.org/10.1117/12.339989.
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Das, Purnima, John F. Roddick, Patricia A. H. Williams y Mehwish Nasim. "Optimised Association Rule Mining for Health Data". En 5th International Conference on Machine Learning & Applications. Academy & Industry Research Collaboration, 2023. http://dx.doi.org/10.5121/csit.2023.131007.
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Association Rule Mining (ARM) has been recognised as a valuable and easy-to-interpret data mining technique in response to the exponential growth of big data. However, research on ARM techniques has mainly focused on enhancing computational efficiency while neglecting the automatic determination of threshold values for measuring the "interestingness" of items. Selecting appropriate threshold values (such as support, confidence, etc.) significantly affects the quality of the association rule mining outcomes. This study proposes an algorithm that utilises Particle Swarm Optimization (PSO) and ARM techniques to determine optimised threshold values in the health domain automatically. The algorithm was evaluated using the UCI machine learning medical database for heart disease. Results show that the proposed algorithm is capable of generating frequent itemsets and rules in an efficient manner and can detect the optimum threshold values. This research has practical implications for the health domains, as it can extract valuable results.
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Grandhi, Appalaraju y Sunil Kumar Singh. "Performance Evaluation and Comparative Study of Machine Learning Techniques on UCI Datasets and Microarray Datasets". En 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2023. http://dx.doi.org/10.1109/icoei56765.2023.10125849.
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Setiyani, Lila, Ayu Indahsari, Rosalina y Tjong Wansen. "Finding the Best Techniques for Predicting Term Deposit Subscriptions (Case Study UCI Machine Learning Dataset)". En 2022 IEEE International Conference on Sustainable Engineering and Creative Computing (ICSECC). IEEE, 2022. http://dx.doi.org/10.1109/icsecc56055.2022.10331379.
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Jik Lee, Byoung. "Extracting the Significant Degrees of Attributes in Unlabeled Data using Unsupervised Machine Learning". En 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 data are tested. The result shows that the proposed approach improves the performance.
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Kaur, Simarjeet, Meenakshi Bansal y Ashok Kumar Bathla. "A Comparitive Study of E-Mail Spam Detection using Various Machine Learning Techniques". En 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 various algorithms have been compared. This paper used random forest, naive bayes, support vector machine (SVM), logistic regression, and the k nearest (KNN) machine learning algorithm to successfully classify email spam messages. The main goal of this study is to improve the prediction accuracy of spam email filters.
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Thumpati, Asitha y Yan Zhang. "Towards Optimizing Performance of Machine Learning Algorithms on Unbalanced Dataset". En 10th International Conference on Artificial Intelligence & Applications. Academy & Industry Research Collaboration Center, 2023. http://dx.doi.org/10.5121/csit.2023.131914.
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Imbalanced data, a common occurrence in real-world datasets, presents a challenge for machine learning classification models. These models are typically designed with the assumption of balanced class distributions, leading to lower predictive performance when faced with imbalanced data. To address this issue, this paper employs data preprocessing techniques, including Synthetic Minority Oversampling Technique (SMOTE) for oversampling and random undersampling, on unbalanced datasets. Additionally, genetic programming is utilized for feature selection to enhance both performance and efficiency. In our experiment, we leverage an imbalanced bank marketing dataset sourced from the UCI Machine Learning Repository. To evaluate the effectiveness of our techniques, we implement it on four different classification algorithms: Decision Tree, Logistic Regression, K-Nearest Neighbors (KNN), and Support Vector Machines (SVM). We compare various evaluation metrics, such as accuracy, balanced accuracy, recall, F-score, Receiver Operating Characteristics (ROC) curve, and Precision-Recall (PR) curve, across different scenarios: unbalanced data, oversampled data, undersampled data, and data cleaned with Tomek-Links. Our findings reveal that all four algorithms demonstrate improved performance when the minority class is oversampled to half the size of the majority class and the majority class is undersampled to match the minority class. Subsequently, applying Tomek-Links on the balanced dataset further enhances performance.
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Wang, Nan, Xibin Zhao, Yu Jiang y Yue Gao. "Iterative Metric Learning for Imbalance Data Classification". En Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: 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 construct an effective data space for classification. Given the imbalance training data, it is important to select a subset of training samples for each testing data. Thus, we aim to find a more stable neighborhood for testing data using the iterative metric learning strategy. To evaluate the effectiveness of the proposed method, we have conducted experiments on two groups of dataset, i.e., the NASA Metrics Data Program (NASA) dataset and UCI Machine Learning Repository (UCI) dataset. Experimental results and comparisons with state-of-the-art methods have exhibited better performance of our proposed method.
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Sen, Anupam. "Data Mining and Principal Component Analysis on Coimbra Breast Cancer Dataset". En 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 classification algorithms are applied through WEKA such as Glmnet, Gbm, ada Boosting, Adabag Boosting, C50, Cforest, DcSVM, fnn, Ksvm, Node Harvest compares the accuracy and also compare values such as Kappa statistic, Mean Absolute Error (MAE), Root Mean Square Error (RMSE). Here the 10-fold cross validation method is used for training, testing and validation purposes.
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Sakai, Hiroshi, Chenxi Liu y Michinori Nakata. "Information Dilution: Granule-Based Information Hiding in Table Data - A Case of Lenses Data Set in UCI Machine Learning Repository". En 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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