Search Results - (( data classification modified algorithm ) OR ( based classification methods algorithm ))
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1
A novel hybrid classification model of genetic algorithms, modified k-Nearest Neighbor and developed backpropagation neural network
Published 2014“…Second, the obtained results that included arrays of the top-ranked features were used as the initial population of a genetic algorithm to produce optimum arrays of features. Third, using a modified k-Nearest Neighbor method as well as an improved method of backpropagation neural networks, the classification process was advanced based on optimum arrays of the features selected by genetic algorithms. …”
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2
A modified weighted support vector machine (WSVM) to reduce noise data in classification problem
Published 2021“…To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
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3
A modified weighted support vector machine (WSVM) to reduce noise data in classification problem
Published 2021“…To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
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4
Mutable composite firefly algorithm for gene selection in microarray based cancer classification
Published 2022“…The results showed that the CFS-MCFA-SVM algorithm outperforms benchmark methods in terms of classification accuracy and genes subset size. …”
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5
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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6
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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7
Improvement of land cover mapping using Sentinel 2 and Landsat 8 imageries via non-parametric classification
Published 2020“…Thus, TC suggested being the main step in data pre-processing for mountainous terrain before the RBF-based SVM classification process. …”
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8
Predicting Customer Buying Decisions for Online Shopping with Unbalanced Data Set
Published 2022“…Six machine learning algorithms were applied and compared based on the classification evaluation methods. …”
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9
A hybrid approach for artificial immune recognition system / Mahmoud Reza Saybani
Published 2016“…The increasing size of data being stored have created the need for computer-based methods for automatic data analysis. …”
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10
Design Of Feature Selection Methods For Hand Movement Classification Based On Electromyography Signals
Published 2020“…The second method is called the Modified Binary Tree Growth Algorithm (MBTGA) that applies swap, crossover, and mutation operators. …”
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11
Boosting and bagging classification for computer science journal
Published 2023“…Classification is a machine-learning technique that groups data based on the supplied label class. …”
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Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi
Published 2019“…Despite attempts to solve the data clustering issues, there are also many variants of modified algorithms in traditional information clustering that attempt to solve issues such as clustering algorithms based on condensation. …”
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13
Feature fusion using a modified genetic algorithm for face and signature recognition system
Published 2015“…Several approaches and benchmark data were used to validate the effectiveness of the proposed method compared to the unimodal system and normal feature selection method. …”
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14
Enhanced Image Classification for Defect Detection on Solar Photovoltaic Modules
Published 2023“…The second algorithm uses K Nearest Neighbour using a ratio of training data and testing data of 95:05 resulting in an accuracy value of 62%. …”
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15
Adaptive method to improve web recommendation system for anonymous users
Published 2011“…The first is iPACT, an improved recommendation system based on PACT methodology to demonstrate the prediction accuracy of the proposed enhanced classification algorithm in this research. …”
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16
Robust diagnostics and variable selection procedure based on modified reweighted fast consistent and high breakdown estimator for high dimensional data
Published 2022“…Therefore, a robust sure independence screening procedure based on the weighted correlation algorithm of MRFCH for high dimensional data is developed to address this problem. …”
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17
Integrated artificial intelligence-based classification approach for prediction of acute coronary syndrome
Published 2014“…The following results were obtained when classification of the ACS types used the conventional “single AI-based” methods. …”
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18
Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion
Published 2017“…Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. …”
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A framework of modified adaptive neuro-fuzzy inference engine
Published 2012“…A modified apriori algorithm was employed to reduce the number of clusters effectively on the basis of common data in the clusters of every input to obtain a minimal set of decision rules based on datasets. …”
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20
RMIL/AG: A new class of nonlinear conjugate gradient for training back propagation algorithm
Published 2018“…The RMIL uses the value of adaptive gain parameter in the activation function to modify the gradient based search direction. The efficiency of the proposed method is verified by means of simulation on four classification problems. …”
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