Search Results - based ((classification problems) OR (classification using))
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1
Text classification using modified multi class association rule
Published 2016“…This paper presents text classification using a modified Multi Class Association Rule Method.The method is based on Associative Classification which combines classification with association rule discovery. …”
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2
An adaptive ant colony optimization algorithm for rule-based classification
Published 2020“…Ant-Miner is a variant of ant colony optimisation and a prominent intelligent algorithm widely use in rules-based classification. However, the Ant-Miner has overfitting and easily falls into local optima problems which resulted in low classification accuracy and complex classification rules. …”
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3
An improved associative classification model using fuzzy parameterized soft set-based decision for text classification
Published 2023“…One of the potential text classifiers is the well-known associative classification approach. However, the existing associative classification approach is still prone to some limitations especially when dealing with the problem with too many rules in text classification problem. …”
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4
An improved associative classification model using fuzzy parameterized soft set-based decision for text classification
Published 2023“…One of the potential text classifiers is the well-known associative classification approach. However, the existing associative classification approach is still prone to some limitations especially when dealing with the problem with too many rules in text classification problem. …”
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5
Dengue classification system using clonal selection algorithm / Karimah Mohd
Published 2012“…This project can be improved by making a comparative study on Artificial Immune System and other techniques or algorithms used to solve dengue classification problems.…”
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6
Several Robust Techniques In Two-Groups Unbiased Linear Classification
Published 2013“…Classification difference was used to compare the classification performance of the proposed techniques over the Fisher linear classification analysis and the Fisher linear classification analysis based on the minimum covariance determinant procedures. …”
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7
Classification of Qur’anic topics based on imbalanced classification
Published 2020“…This problem can be occurred in the Qur’anic classification due to the different in the number of verses. …”
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8
Classification o f excessive water usage based on a Quantifier Fuzzy Classification System / Halimatusakina Omar and Hanis Wahidah Uzi
Published 2018“…A fuzzy quantifier based classification system or known as Fuzzy QSBA is proposed to be used as a method to generate and simplifies rules. …”
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9
Logistic regression methods for classification of imbalanced data sets
Published 2012“…Thus, both proposed imbalanced LR-based methods is simple and effective for classification of imbalanced data sets and have promising results.…”
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10
A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem
Published 2016“…Data level based methods are meant to solve the imbalanced classification problem based on the idea of making both classes equal in number. …”
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11
A De-noising Scheme for Enhancing Power Quality Problem Classification System Based on Wavelet Transform and Rule-Based Method
Published 2011“…Using the de-noising scheme proposed in this paper, a higher tolerance to noise can be achieved by the Power Quality Problem Classification system.…”
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12
Multiclass classification for chest x-ray images based on lesion location in lung zones
Published 2015“…Therefore, it is difficult to classify CXR images which contain many image features.Realizing the problem, we proposed an application method for multiclass classification with SVM to the CXR images based on the lesion position in the lung zones.The multiclass classification application is executed on the CXR images taken from Japan Society of Radiology Technology dataset.Lesion coordinates were selected as the classification input while the lung zones becomes the labels. …”
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Attribute Set Weighting and Decomposition Approaches for Reduct Computation
Published 2005“…The evaluation is mainly in two folds: first is to evaluate the proposed approaches as Rough Set based methods where the classification accuracy is used as an evaluation measure. …”
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15
Improved Artificial Neural Network Classification Model based Metaheuristic Optimization for Handwritten Character Recognition
Published 2024“…The problems identified specifically pertain to the classification model, particularly in the context of the Artificial Neural Network (ANN) learning problem, leading to low accuracy in recognizing handwritten characters. …”
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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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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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18
Power quality problem classification based on Wavelet Transform and a Rule-Based method
Published 2010“…Thus it stands up to be a suitable model to be used in real time implementation through a dsPIC-based embedded system.…”
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Application of Optimization Methods for Solving Clustering and Classification Problems
Published 2011“…Then a review of different methods currently available that can be used to solve clustering and classification problems is also given. …”
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20
Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model
Published 2018“…Expectation maximization (EM) is one of the representatives clustering algorithms which have broadly applied in solving classification problems by improving the density of data using the probability density function. …”
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