Search Results - (( using optimization _ algorithm ) OR ( data classification mining algorithm ))
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1
Hybrid ant colony optimization and genetic algorithm for rule induction
Published 2020“…The AntMiner classifier is efficient, useful and commonly used for solving rulebased classification problems in data mining. …”
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2
Evaluation and optimization of frequent association rule based classification
Published 2014“…Works on sustaining the interestingness of rules generated by data mining algorithms are actively and constantly being examined and developed. …”
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3
Training functional link neural network with ant lion optimizer
Published 2020“…The Ant Lion Optimizer (ALO) is the metaheuristic optimization algorithm that mimics the hunting mechanism of antlions in nature. …”
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4
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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5
An ensemble learning method for spam email detection system based on metaheuristic algorithms
Published 2015“…In the second phase, a classifier ensemble learning model is proposed consisting of separate outputs: (i) To select a relevant subset of original features based on Binary Quantum Gravitational Search Algorithm (QBGSA), (ii) To mine data streams using various data chunks and overcome a failure of single classifiers based on SVM, MLP and K-NN algorithms. …”
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6
Attribute related methods for improvement of ID3 Algorithm in classification of data: A review
Published 2020“…Decision tree is an important method in data mining to solve the classification problems. …”
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7
Classification of students' performance in computer programming course according to learning style
Published 2024Subjects:Conference Paper -
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Optimization of attribute selection model using bio-inspired algorithms
Published 2019“…Such a finding indicates that bio-inspired algorithms can contribute in identifying the few most important features to be used in data mining model construction.…”
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A new model for iris data set classification based on linear support vector machine parameter's optimization
Published 2020“…In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.…”
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10
Ideal combination feature selection model for classification problem based on bio-inspired approach
Published 2020“…Such a finding indicates that the exploitation of bio-inspired algorithms with ideal combination of wrapper/filtered method can contribute in finding the optimal features to be used in data mining model construction.…”
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Book Section -
11
Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy
Published 2019“…The proposed algorithm is compared with the state-of-the-art feature selection algorithms using three different datasets. …”
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Thesis -
12
Application of Optimization Methods for Solving Clustering and Classification Problems
Published 2011“…Cluster and classification analysis are very interesting data mining topics that can be applied in many fields. …”
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13
Tree-based contrast subspace mining method
Published 2020“…Hence, this thesis presents the optimization of parameters values for the tree-based method by genetic algorithm. …”
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14
Predicting Customer Buying Decisions for Online Shopping with Unbalanced Data Set
Published 2022“…Weka, a data mining tool, provides the facility to classify the data set with different machine learning algorithms. …”
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Final Year Project / Dissertation / Thesis -
15
Evaluation and optimization of frequent, closed and maximal association rule based classification
Published 2014“…Real world applications of association rule mining have well-known problems of discovering a large number of rules, many of which are not interesting or useful for the application at hand.The algorithms for closed and maximal item sets mining significantly reduce the volume of rules discovered and complexity associated with the task, but the implications of their use and important differences with respect to the generalization power, precision and recall when used in the classification problem have not been examined.In this paper, we present a systematic evaluation of the association rules discovered from frequent, closed and maximal item set mining algorithms, combining common data mining and statistical interestingness measures, and outline an appropriate sequence of usage.The experiments are performed using a number of real-world datasets that represent diverse characteristics of data/items, and detailed evaluation of rule sets is provided as a whole and w.r.t individual classes. …”
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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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17
A novel approach to data mining using simplified swarm optimization
Published 2011“…This deficiency has prompted the need for a new intelligent data mining technique based on stochastic population-based optimization that could discover useful information from data. …”
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18
Improving sentiment reviews classification performance using support vector machine-fuzzy matching algorithm
Published 2023“…High dimensionality in data sets is one of the challenges faced in classification, data mining, and sentiment analysis. …”
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A direct ensemble classifier for imbalanced multiclass learning
Published 2012“…Researchers have shown that although traditional direct classifier algorithm can be easily applied to multiclass classification, the performance of a single classifier is decreased with the existence of imbalance data in multiclass classification tasks.Thus, ensemble of classifiers has emerged as one of the hot topics in multiclass classification tasks for imbalance problem for data mining and machine learning domain.Ensemble learning is an effective technique that has increasingly been adopted to combine multiple learning algorithms to improve overall prediction accuraciesand may outperform any single sophisticated classifiers.In this paper, an ensemble learner called a Direct Ensemble Classifier for Imbalanced Multiclass Learning (DECIML) that combines simple nearest neighbour and Naive Bayes algorithms is proposed. …”
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Conference or Workshop Item -
20
Feature and Instances Selection for Nearest Neighbor Classification via Cooperative PSO
Published 2014“…Therefore, it can be solved by using population-based techniques such as Genetic Algorithm and Particle Swarm Optimization. …”
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Conference or Workshop Item
