Search Results - (( variable learning selection algorithm ) OR ( java active learning algorithm ))
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1
Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition
Published 2007“…In the selection phase, a new variable length genetic algorithm is applied to select the lexical cues. …”
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2
Particle Swarm Optimization in Machine Learning Prediction of Airbnb Hospitality Price Prediction
Published 2022“…Particle Swarm Optimization is useful to optimize the best variables combination for automating the features selection in machine learning models. …”
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3
Dynamic Bayesian networks and variable length genetic algorithm for designing cue-based model for dialogue act recognition
Published 2010“…In the second stage, the developed variable length genetic algorithm is used to select different sets of lexical cues to constitute the dynamic Bayesian networks' random variables. …”
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4
Application of Decision Trees in Athlete Selection: A Cart Algorithm Approach
Published 2023“…Notably, weight, and Body Mass Index (BMI) exhibit the highest significance among the other variables. Looking ahead, future research could explore enhancing DTs' predictive capabilities in athlete selection by incorporating more variables or employing ensemble learning techniques. …”
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Predicting mortality of Malaysian patients with acute coronary syndrome (ACS) subtypes using machine learning and deep learning approaches / Muhammad Firdaus Aziz
Published 2022“…ML algorithms were used to examine significant variables utilising feature selection methods. …”
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6
Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde
Published 2022“…The support vector machines (SVM) algorithm obtained the overall best results of 94.5% accuracy, 91.8% precision, 91.7% recall, and 91.1% f-Measure while the naïve bayes (NB) algorithm obtained the best AUC score of 0.944 with the tweet data of Dato Seri Anwar. …”
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7
A New Quadratic Binary Harris Hawk Optimization For Feature Selection
Published 2019“…A comparative study is conducted to compare the effectiveness of QBHHO with other feature selection algorithms such as binary differential evolution (BDE), genetic algorithm (GA), binary multi-verse optimizer (BMVO), binary flower pollination algorithm (BFPA), and binary salp swarm algorithm (BSSA). …”
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Automated model selection for corporation credit risk assessment using machine learning / Zulkifli Halim
Published 2023“…These techniques have demonstrated their superiority over traditional approaches in many CCRA studies. Machine learning model selection is an iterative process of exploring, evaluating, and improving algorithms. …”
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9
Development Of Machine Learning User Interface For Pump Diagnostics
Published 2022“…Build up a user interface by using Visual Studio Code (VSC) to run the coding of Cascading Style Sheet (CSS), Hyper Text Markup Language (HTML) and JavaScript (JS) as a webpage and connect to Azure Machine Learning Model and this will allow the user from using the model from a webpage when they have active internet with any devices.…”
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10
Enhanced Adaptive Confidence-Based Q Routing Algorithms For Network Traffic
Published 2004“…An integrated solution for the above problem is the ECQ Routing Algorithm. ECQ Routing Algorithm is integrates the Variable of Decay Constant and Update All Q Value approaches for updating the C values of non-selected Q values. …”
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11
Voltage stability margin identification using evolution programming learning algorithm / Zamzuhairi Darus
Published 2003“…A systematic method for selecting the ANN's input variables was developed using Matlab Programming language.…”
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12
Information Theoretic-based Feature Selection for Machine Learning
Published 2018“…Three major factors that determine the performance of a machine learning are the choice of a representative set of features, choosing a suitable machine learning algorithm and the right selection of the training parameters for a specified machine learning algorithm. …”
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13
Prediction of monthly rainfall at Senai, Johor using artificial immune system and deep learning neural network
Published 2020“…MLP is a deep learning algorithm used in the Artificial Neural Network (ANN). …”
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14
Predicting 30-day mortality after an acute coronary syndrome (ACS) using machine learning methods for feature selection, classification and visualization
Published 2021“…Identifying a feature selection method with a classifier algorithm that produces high performance in mortality studies is essential and has not been reported before. …”
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15
Feature selection for high dimensional data: An evolutionary filter approach.
Published 2011“…Problem statement: Feature selection is a task of crucial importance for the application of machine learning in various domains. …”
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16
Survival versus non-survival prediction after acute coronary syndrome in Malaysian population using machine learning technique / Nanyonga Aziida
Published 2019“…The selected algorithms effectively show increase in prediction performance with decreasing features. …”
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17
Machine Learning Classifications of Multiple Organ Failures in a Malaysian Intensive Care Unit
Published 2025“…This study demonstrates the performances of different machine learning algorithms in the classification of multiple organ failures. …”
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Machine learning classifications of multiple organ failures in a malaysian intensive care unit
Published 2024“…This study demonstrates the performances of different machine learning algorithms in the classification of multiple organ failures. …”
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Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…Features selection process can be considered a problem of global combinatorial optimization in machine learning. Genetic algorithm GA had been adopted to perform features selection method; however, this method could not deliver an acceptable detection rate, lower accuracy, and higher false alarm rates. …”
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20
Random forest algorithm for co2 water alternating gas incremental recovery factor prediction
Published 2020“…RF develops multiple decision trees based on the random selection of the input data and random selection of the variables. …”
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