Search Results - (( using case method algorithm ) OR ( data classification learning algorithm ))
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1
Case Slicing Technique for Feature Selection
Published 2004“…One of the problems addressed by machine learning is data classification. Finding a good classification algorithm is an important component of many data mining projects. …”
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2
An efficient and effective case classification method based on slicing
Published 2006“…The paper also discusses two of common classification algorithms that are used either in data mining or in general AI. …”
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A case study of microarray breast cancer classification using machine learning algorithms with grid search cross validation
Published 2023“…Annually, almost half a million women do not survive the disease and die from breast cancer. Machine learning is a subfield of artificial intelligence (AI) and computer science that uses data and algorithms to mimic how humans learn, and gradually improving its accuracy. …”
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4
Thematic textual hadith classification: an experiment in rapidminer using support vector machine (SVM) and naïve bayes algorithm
Published 2020“…The classification for this thematic Hadith dataset is implemented using Rapidminer, a machine learning tool using Naïve Bayes and Support Vector Machine (SVM) methods. …”
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Static hand gesture recognition using artificial neural network / Haitham Sabah Hasan
Published 2014“…Artificial neural network is built for the purpose of classification by using the back- propagation learning algorithm. …”
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6
Deep learning object detector using a combination of Convolutional Neural Network (CNN) architecture (MiniVGGNet) and classic object detection algorithm
Published 2020“…The performance of this method can work in some specific use cases and effectively solving the problem of the inaccurate classification and detection of typical features.…”
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Optimizing in-car-abandoned children’s sounds detection using deep learning algorithms / Nur Atiqah Izzati Md Fisol
Published 2023“…To address this problem, an optimized in-car-abandoned children's sounds detection model using deep learning algorithms is proposed. The objective of this study is to develop an accurate and efficient model capable of recognizing the presence of children in cars based on sound data. …”
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8
Utilisation of Exponential-Based Resource Allocation and Competition in Artificial Immune Recognition System
Published 2011“…Artificial Immune Recognition System is one of the several immune inspired algorithms that can be used to perform classification, a data mining task. …”
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9
Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.
Published 2005“…The accuracy for rules and classification resulted from the TIP method are compared with other methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) from Rough Set, Genetic Algorithm (GA), Johnson reducer, HoltelR method, Multiple Regression (MR), Neural Network (NN), Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5); all other classifiers that are mostly used in the classification tasks. …”
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10
Optimal Weighted Learning of PCA and PLS for Multicollinearity Discriminators and Imbalanced Groups in Big Data (S/O: 13224)
“…The designed algorithm was structured in k-fold cross-validation in attempt to minimise the biasness of the classification performance, measured using error rate. …”
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A voting-based hybrid machine learning approach for fraudulent financial data classification / Kuldeep Kaur Ragbir Singh
Published 2019“…Standard base machine learning algorithms, which include a total of twelve individual methods as well as the AdaBoost and Bagging methods, are firstly used. …”
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The classification of FTIR plastic bag spectra via label spreading and stacking
Published 2021“…Four pipelines were investigated, consisting of two machine learning algorithms, a stacked model that stacks the KNN, SVM and RF algorithms together, and Label spreading, as well as two different dimensionality reduction methods namely; SVD and UMAP. …”
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An evolutionary based features construction methods for data summarization approach
Published 2015“…In other words, this research will discuss the application of genetic algorithm to optimize the feature construction process from the Coral Reefs data to generate input data for the data summarization method called Dynamic Aggregation of Relational Attributes (DARA). …”
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Random Undersampling on Imbalance Time Series Data for Anomaly Detection
Published 2023Conference Paper -
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Deep plant: A deep learning approach for plant classification / Lee Sue Han
Published 2018“…Besides using solely a single leaf organ to recognize plant species, numerous studies have employed DL methods to solve multi-organ plant classification problem. …”
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16
Landslide Susceptibility Mapping with Stacking Ensemble Machine Learning
Published 2024“…One of the prominent methods to improve machine learning accuracy is by using ensemble method which basically employs multiple base models. …”
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Ensemble learning for multidimensional poverty classification
Published 2020“…CRoss Industry Standard Process for Data Mining (CRISP-DM) methods was used to ensure data mining and ML processes were conducted properly. …”
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Data mining for structural damage identification using hybrid artificial neural network based algorithm for beam and slab girder / Meisam Gordan
Published 2020“…In the modeling phase, amongst all DM algorithms, the applicability of machine learning, artificial intelligence and statistical data mining techniques were examined using Support Vector Machine (SVM), Artificial Neural Network (ANN) and Classification and Regression Tree (CART) to detect the hidden patterns in vibration data. …”
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Classification Of Gender Using Global Level Features In Fingerprint For Malaysian Population
Published 2016“…These four classifiers are used in the data mining task with five test cases each in order to find the accuracies of the gender classification. …”
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A stacked ensemble deep learning model for water quality prediction / Wong Wen Yee
Published 2023“…Classifiers often perform poorly in skewed data due to a bias in the majority class. Therefore, this paper aims to explore the use of ensemble and deep learning techniques to simplify the classification process of imbalanced data. …”
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