Search Results - ((data classification) OR (based classification)) (problems OR problem)
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1
A derivative-free optimization method for solving classification problem
Published 2010“…There is a training set for each class. Those problems occur in a wide range of human activity. One of the most promising ways to data classification is based on methods of mathematical optimization. …”
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2
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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3
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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4
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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5
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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6
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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7
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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8
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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9
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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10
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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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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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
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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14
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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15
An adaptive ant colony optimization algorithm for rule-based classification
Published 2020“…Classification is an important data mining task with different applications in many fields. …”
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16
Mutable composite firefly algorithm for gene selection in microarray based cancer classification
Published 2022“…A computational approach for gene selection based on microarray data analysis has been applied in many cancer classification problems. …”
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17
Classification of breast cancer disease using bagging fuzzy-id3 algorithm based on fuzzydbd
Published 2022“…Classification is a data mining technique used to classify varied data types according to a specific criterion. …”
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18
Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model
Published 2018“…In data mining, classification learning is broadly categorized into two categories; supervised and unsupervised. …”
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Comparison Performance of Qualitative Bankruptcy Classification based on Data Mining Algorithms
Published 2018“…Knowledge discovery using data mining techniques are commonly applied in bankruptcy classification and prediction. …”
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An enhanced gated recurrent unit with auto-encoder for solving text classification problems
Published 2020“…However, GRU suffered from three major issues when it is applied for solving the text classification problems. The first drawback is the failure in data dimensionality reduction, which leads to low quality solution for the classification problems. …”
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