Search Results - data classification (problems OR problem)
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1
Application of Optimization Methods for Solving Clustering and Classification Problems
Published 2011“…Next the problem of data classification is studied as a problem of global, non-smooth and non-convex optimization; this approach consists of describing clusters for the given training sets. …”
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2
A derivative-free optimization method for solving classification problem
Published 2010“…Problem statement: The aim of data classification is to establish rules for the classification of some observations assuming that we have a database, which includes of at least two classes. …”
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3
Network problems detection and classification by analyzing syslog data
Published 2016“…This study contributes to the field of network troubleshooting, and the field of text data classification.…”
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4
Enhanced Robust Univariate Classification Methods for Solving Outliers and Overfitting Problems
Published 2023“…Previous studies often used the Bayes Classifier (BC) and the Predictive Classifier (PC) to address two groups of univariate classification problems. Unfortunately for substantial large sample sizes and uncontaminated data, the BC method overfits when the Optimal Probability of Exact Classification (OPEC) is used as an evaluation benchmark. …”
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5
A direct ensemble classifier for learning imbalanced multiclass data
Published 2013“…Thus, an ensemble of classifiers is one of the methods used to solve multiclass classification tasks. In this thesis, the problem of learning from imbalanced multiclass data classification is studied. …”
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6
Text classification using modified multi class association rule
Published 2016“…Although previous work proved that Associative Classification produces better classification accuracy compared to typical classifiers, the study on applying Associative Classification to solve text classification problem are limited due to the common problem of high dimensionality of text data and this will consequently results in exponential number of generated classification rules. …”
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7
Dengue classification system using clonal selection algorithm / Karimah Mohd
Published 2012“…Some of the dengue data are used to test the dengue classification system to produce the classification accuracy. …”
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8
Logistic regression methods for classification of imbalanced data sets
Published 2012“…Classification of imbalanced data sets is one of the important researches in Data Mining community, since the data sets in many real-world problems mostly are imbalanced class distribution. …”
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9
A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem
Published 2016“…There are two methods in dealing with imbalanced classification problem, which are based on data or algorithmic level. …”
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10
A modified weighted support vector machine (WSVM) to reduce noise data in classification problem
Published 2021“…Classification refers to a predictive modeling problem where a class label is predicted for a given example of input data. …”
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11
A modified weighted support vector machine (WSVM) to reduce noise data in classification problem
Published 2021“…Classification refers to a predictive modeling problem where a class label is predicted for a given example of input data. …”
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12
Development of preliminary classification system for drug related problem in patients receiving radio pharmaceuticals based on PCNE system V9.1 / Nurulfalahin Daud @ Ibrahim
Published 2022“…A classification system for drug related problem is necessary to allow the identification and documentation of drug related problem in a systematic way. …”
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13
Accurate and reliable diagnosis and classification using probabilistic ensemble simplified fuzzy ARTMAP
Published 2005“…For the medical diagnosis and classification problems, PESFAM achieves 100 percent in accuracy, specificity, and sensitivity based on the 10-fold crossvalidation and these results are superior to those from other classification algorithms. …”
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14
Improved class binarization model with data oversampling in gait recognition
Published 2019“…In this thesis, we found several problems at the data acquisition stage, pre-processing stage, and classification stage. …”
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15
A new classification model for a class imbalanced data set using genetic programming and support vector machines: case study for wilt disease classification
Published 2015“…The experimentation carried out on wilt disease data set shows the new classifier, support vector based on genetic programming machine, gives a more balanced accuracy between classes compared to various classification techniques in solving the imbalanced classification problem.…”
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16
A Comparison Between Levenberg-Marquardt (LM) Intelligent System And Bayesian Regularization (BR) Intelligent System For Flow Regime Classification
Published 2006“…ECT measured the different capacitance value of fluid and produced the data for the classification problem. Multilayed Perceptron (MLP), a type of artificial neural network (ANN) which is widely used in a classification problem is developed using MATLAB 7®. …”
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17
Flexible enhanced fuzzy min–max neural network model for pattern classification problems
Published 2020“…Fourth, a new contraction procedure to overcome the containment overlap and avoiding the data distortion problem (missing hyperbox information). …”
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18
Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition
Published 2017“…Whereas for supervised learning method, it requires teacher or prior data (i.e. large, prohibitive and labelled training data) during classification process which in real life, the cost of obtaining sufficient labelled training data is high. …”
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19
An improve unsupervised discretization using optimization algorithms for classification problems
Published 2024“…This paper addresses the classification problem in machine learning focusing on predicting class labels for datasets with continuous features. …”
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An improve unsupervised discretization using optimization algorithms for classification problems
Published 2024“…This paper addresses the classification problem in machine learning, focusing on predicting class labels for datasets with continuous features. …”
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