Search Results - (( program implementation based algorithm ) OR ( data classification methods algorithm ))
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1
Case Slicing Technique for Feature Selection
Published 2004“…The classification accuracy obtained from the CST method is compared to other selected classification methods such as Value Difference Metric (VDM), Pre-Category Feature Importance (PCF), Cross-Category Feature Importance (CCF), Instance-Based Algorithm (IB4), Decision Tree Algorithms such as Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5), Rough Set methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) and Neural Network methods such as the Multilayer method.…”
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2
Classification of credit card holder behavior using K Nearest Neighbor algorithm / Ahmad Faris Rahimi
Published 2017“…This indicates that the performance of KNN is acceptable and promising in this classification problem. Since KNN is the simplest form of artificial intelligence, future work could combine this algorithm with other classification algorithm. …”
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3
Feature clustering for pso-based feature construction on high-dimensional data
Published 2019“…Experimental results were obtained by using six UCI data sets and six high-dimensional data to demonstrate the efficiency of the proposed method when compared to the original full features, other PSO based FC methods, and standard genetic programming based feature construction (GPFC). …”
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4
Problem restructuring in interger programming for reduct searching
Published 2003“…They can describe the whole information system when implementing discernment. In effect, they are very useful in generating rules when solving the classification problem that is inherent in data mining. …”
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5
Implementation of Hybrid Indexing, Clustering and Classification Methods to Enhance Rural Development Programme in South Sulawesi
Published 2024“…Village development and assistance program is a strategy implemented to accelerate socio cultural development to increase the capacity of village governance and more organized administration. …”
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6
Exploring frogeye leaf spot disease severity in soybean through hyperspectral data analysis and machine learning with Orange Data Mining
Published 2025“…Objectives: The main objective of the study is to classify the severity level of FLS disease in soybean using hyperspectral reflectance data and machine learning algorithms. Materials and Methods: We used hyperspectral reflectance data from healthy and FLS of soybeans. …”
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7
Chili crop segregation system design and development strategies
Published 2021“…This paper focused more on the Machine Learning (ML) technique, which is the main component of Artificial Intelligence. The image data taken from chili samples can be trained by using Learning Algorithm in the MATLAB program. …”
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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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9
Grid-Based Classifier as a Replacement for Multiclass Classifier in a Supervised Non-Parametric Approach
Published 2009“…The experimental results on artificial data sets and real-world data sets (from UCI Repository) show that the new method could improve both the efficiency and accuracy of pattern classification. …”
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10
Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms
Published 2005“…Methods for improving supervised and unsupervised classification of remotely sensed data were developed in this study. …”
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11
Classification of breast cancer disease using bagging fuzzy-id3 algorithm based on fuzzydbd
Published 2022“…The study verified that the FID3-DBD algorithm could classify the continuous data, and the BFID3-DBD algorithm overcame the overfitting issue, reduced high variance, and increased test data classification accuracy.…”
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12
Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition
Published 2017“…Whereas for supervised learning method, it requires teacher or prior data (i.e. large, prohibitive and labelled training data) during classification process which in real life, the cost of obtaining sufficient labelled training data is high. …”
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13
Realization Of The 1D Local Binary Pattern (LBP) Algorithm In Raspberry Pi For Iris Classification Using K-NN Classifier
Published 2018“…There are a lot of feature extraction methods and classification methods for iris classification. …”
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14
An efficient and effective case classification method based on slicing
Published 2006“…The paper also discusses two of common classification algorithms that are used either in data mining or in general AI. …”
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15
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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16
Application of Optimization Methods for Solving Clustering and Classification Problems
Published 2011“…Samples in the same cluster have the same label. The aim of data classification is to set up rules for the classification of some observations that the classes of data are supposed to be known. …”
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17
Arabic text classification using hybrid feature selection method using chi-square binary artificial bee colony algorithm
Published 2021“…Text classification is a popular method in data mining. It is utilized to get valuable information from the vast quantity of data. …”
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An efficient algorithm for cardiac arrhythmia classification using ensemble of depthwise Separable convolutional neural networks
Published 2020“…Many algorithms have been developed for automated electrocardiogram (ECG) classification. …”
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A derivative-free optimization method for solving classification problem
Published 2010“…One of the most promising ways to data classification is based on methods of mathematical optimization. …”
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Application of the bees algorithm to the selection features for manufacturing data
Published 2007“…The proposed method has been tested on data collected in semiconductor manufacturing. …”
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