Search Results - (( quality classification problems algorithm ) OR ( data classification using algorithm ))
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1
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. …”
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2
Intent-IQ: customer’s reviews intent recognition using random forest algorithm
Published 2025“…Two machine learning model is chosen to build the classification models which are Random Forest (RF) algorithm and Multinomial Naïve Bayes (MNB) algorithm. …”
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3
New Learning Models for Generating Classification Rules Based on Rough Set Approach
Published 2000“…Huge information systems or data sets usually have some missing values due to unavailable data that affect the quality of the generated classification rules. …”
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4
BAT-BP: A new BAT based back-propagation algorithm for efficient data classification
Published 2016“…One of the nature inspired meta-heuristic Bat algorithm is becoming a popular method in solving many complex optimization problems. …”
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5
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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6
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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7
Ant colony optimization for rule induction with simulated annealing for terms selection
Published 2012“…This paper proposes a sequential covering based algorithm that uses an ant colony optimization algorithm to directly extract classification rules from the data set.The proposed algorithm uses a Simulated Annealing algorithm to optimize terms selection, while growing a rule.The proposed algorithm minimizes the problem of a low quality discovered rule by an ant in a colony, where the rule discovered by an ant is not the best quality rule, by optimizing the terms selection in rule construction. …”
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8
Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad
Published 2018“…Experimental results show that the developed methods and model are able to classify the Harumanis quality with accuracy of 79% using fuzzy classification based on shape and size.…”
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9
A new ant based rule extraction algorithm for web classification
Published 2011“…Methods to reduce the number of attributes and discretization are two important data pre-processing steps before the data can be used for classification activity. …”
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10
Effective gene selection techniques for classification of gene expression data
Published 2005“…The selected subset of genes is then be used to train the classifiers for constructing rules for future tissue classification problem. …”
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11
Determining the preprocessing clustering algorithm in radial basis function neural network
Published 2008“…Besides, the initial selection criterion using Mardia’s skewness is able to show the improvement of efficiency in data classification. …”
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12
Software defect prediction framework based on hybrid metaheuristic optimization methods
Published 2015“…For the purpose of this study, ten classification algorithms have been selected. The selection aims at achieving a balance between established classification algorithms used in software defect prediction. …”
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13
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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14
Rough set discretization: equal frequency binning, entropy/MDL and semi naives algorithms of intrusion detection system
Published 2017“…Discretization of real value attributes is a vital task in data mining, particularly in the classification problem. …”
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15
Rough set discretization: Equal frequency binning, entropy/MDL and semi naives algorithms of intrusion detection system
Published 2016“…Discretization of real value attributes is a vital task in data mining, particularly in the classification problem. …”
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16
Rough Set Discretization: Equal Frequency Binning, Entropy/MDL and Semi Naives Algorithms of Intrusion Detection System
Published 2016“…Discretization of real value attributes is a vital task in data mining, particularly in the classification problem. …”
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17
Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects
Published 2014“…The outcomes positively indicate the effectiveness of the proposed networks in handling classification problems with imbalanced data sets.…”
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18
A De-noising Scheme for Wavelet Based Power Quality Disturbances Detection and Classification system
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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19
Class binarization with self-adaptive algorithm to improve human activity recognition
Published 2018“…In order to estimate the quality of ‘pruned’ features, self-adaptive DE algorithm is proposed. …”
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20
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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