Search Results - (( using vectorization based algorithm ) OR ( data classification clustering algorithm ))

An ensemble data summarization approach based on feature transformation to learning relational data by Chung, Seng Kheau

“…A ensemble clustering is designed, used and evaluated to generate the final classification framework that will take all input generated from the GA based clustering with Feature Selection and Feature Construction algorithms and perform the classification task for the relational datasets. …”
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Extreme learning machine classification of file clusters for evaluating content-based feature vectors by Ali, Rabei Raad, Mohamad, Kamaruddin Malik, Jamel, Sapiee, Ahmad Khalid, Shamsul Kamal

“…This paper studies the effects of three content-based features extraction methods in improving the classification of JPEG File clusters. …”
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Application of Optimization Methods for Solving Clustering and Classification Problems by Shabanzadeh, Parvaneh

“…The data vectors are assigned to the closest cluster and correspondingly to the set, which contains this cluster and an algorithm based on a derivative-free method is applied to the solution of this problem. …”
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A modified weighted support vector machine (WSVM) to reduce noise data in classification problem by Mohd Dzulkifli, Syarizul Amri

“…Nowadays, the use of SVM is very perspective for the big data classification. SVM provides a global solution for data classification but SVM is highly sensitive to noise data and may not be effective when the level of noise data is high. …”
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A modified weighted support vector machine (WSVM) to reduce noise data in classification problem by Mohd Dzulkifli, Syarizul Amri

“…Nowadays, the use of SVM is very perspective for the big data classification. SVM provides a global solution for data classification but SVM is highly sensitive to noise data and may not be effective when the level of noise data is high. …”
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Effective gene selection techniques for classification of gene expression data by Yeo, Lee Chin

“…Various k-means clustering algorithms and model-based clustering algorithms are proposed to group the genes. …”
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Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms by Alswaitti, Mohammed Y. T.

“…The latter drawbacks are consequences of the difficulty in balancing the exploration and exploitation processes which directly affect the final quality of the clustering solutions. Hence, this research has proposed three enhanced frameworks, namely, Optimized Gravitational-based (OGC), Density-Based Particle Swarm Optimization (DPSO), and Variance-based Differential Evolution with an Optional Crossover (VDEO) frameworks for data clustering. …”
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A Hybrid Rough Sets K-Means Vector Quantization Model For Neural Networks Based Arabic Speech Recognition by Babiker, Elsadig Ahmed Mohamed

“…Rough sets theory represents a mathematical approach to vagueness and uncertainty. Data analysis, data reduction, approxi mate classification, machine learning, and discovery of pattern in data are functions performed by a rough sets analysis. …”
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A new model for iris data set classification based on linear support vector machine parameter's optimization by Faiz Hussain, Zahraa, Ibraheem, Hind Raad, Alsajri, Mohammad, Ali, Ahmed Hussein, Mohd Arfian, Ismail, Shahreen, Kasim, Sutikno, Tole

“…One of the outstanding classifications methods in data mining is support vector machine classification (SVM). …”
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Hybrid intelligent approach for network intrusion detection by Al-Mohammed, Wael Hasan Ali

“…Under the purpose of classification, support vector machine have been used. …”
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Combining cluster quality index and supervised learning to predict students’ academic performance by Suhaila Zainudin, Rapi’ah Ibrahim, Hafiz Mohd Sarim

“…Three classification algorithms have been selected: Logistic Regression (LR), Support Vector Machine (SVM) and Decision Tree (DT). …”
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Optimized feature construction methods for data summarizations of relational data by Sze, Florence Sia Fui

“…The summarized data will then be fed to any classification algorithm to perform the classification task. …”
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k-nearest neighbour using ensemble clustering based on feature selection approach to learning relational data by Alfred, Rayner, Shin, Kung Ke, Sainin, Mohd Shamrie, On, Chin Kim, Pandiyan, Paulraj Murugesa, Ag Ibrahim, Ag Asri

“…Due to the growing amount of data generated and stored in relational databases, relational learning has attracted the interest of researchers in recent years.Many approaches have been developed in order to learn relational data.One of the approaches used to learn relational data is Dynamic Aggregation of Relational Attributes (DARA).The DARA algorithm is designed to summarize relational data with one-to-many relations. …”
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An improved plant identification system by Fuzzy c-means bag of visual words model and sparse coding by Safa, Soodabeh, Khalid, Fatimah

“…Classic bag of visual words algorithm is based on k-means clustering and every SIFT features belongs to one cluster and it leads to decreasing classification results. …”
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Improvement of land cover mapping using Sentinel 2 and Landsat 8 imageries via non-parametric classification by Myaser, Jwan

“…Nevertheless, AC is not required for LCM if the original multi-spectral image is used. The last phase involves developing a new fusion algorithm using SVM and Fuzzy K-Means Clustering (FKM) algorithms for Sentinel 2 data to enhance LCM accuracy. …”
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Automatic detection and indication of pallet-level tagging from rfid readings using machine learning algorithms by Choong, Chun Sern

“…The ensemble learning technique, changes of activation function in Neural Network as well as the unsupervised learning (k-means clustering algorithm and Friis Transmission Equation) was also applied to classify the multiclass classification in pallet-level. …”
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A study on component-based technology for development of complex bioinformatics software by Ali Shah, Zuraini, Deris, Safaai, Othman, Muhamad Razib, Zakaria, Zalmiyah, Saad, Puteh, Hassan, Rohayanti, Muda, Mohd. Hilmi, Kasim, Shahreen, Roslan, Rosfuzah

“…The second layer uses discriminative SVM algorithm with a state-of-the-art string kernel based on PSI-BLAST profiles that is used to leverage the unlabeled data. …”
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An application of predicting student performance using kernel k-means and smooth support vector machine by Sajadin, Sembiring

“…This thesis presents the model of predicting student academic performances inHigher Learning Institution (HLI).The prediction ofstudentssuccessfulis one of the most vital issues inHLI.In the previous work, thereare many methodsproposed topredictthe performanceof students such as Scholastic Aptitude Test (SAT) or American College Test (ACT), Intelligent Test, Fuzzy Set Theory, Neural Network, Decision Tree and Naïve Bayes.However, thefactremainsfound ina variety of debateamongeducators inhigher learning institution, especially those relatedto predictorvariablesthatused and the resulting level of prediction accuracy.This shown that the rule model in predicting student performanceisstilla gapand it is urgent for educators to obtain a more accurate prediction results.The objective of thisstudyis to create a rule model in predicting of students performance based on their psychometric factors. In this study, psychometric factors used as predictor variables, thereare Interest, Study Behavior, Engaged Time, Believe, and Family Support.The rulemodel developed using Kernel K-means Clustering and Smooth Support Vector MachineClassification.Both of these techniquesbased on kernel methodsand relativelynew algorithms of data mining techniques, recently received increasingly popularity in machine learning community. …”
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Predicting 30-day mortality after an acute coronary syndrome (ACS) using machine learning methods for feature selection, classification and visualization by Nanyonga Aziida, Sorayya Malek, Firdaus Aziz, Khairul Shafiq Ibrahim, Sazzli Kasim

“…Hybrid combinations of feature selection, classification and visualisation using machine learning (ML) methods have the potential for enhanced understanding and 30-day mortality prediction of patients with cardiovascular disease using population-specific data. …”
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Classification of labour pain using electroencephalogram signal based on wavelet method / Sai Chong Yeh by Sai , Chong Yeh

“…Supervised and unsupervised machine learning algorithms particularly the Support Vector Machine (SVM) and Density Based Spatial Clustering of Application with Noise (DBSCAN) are used in this study. …”
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