Search Results - (( data selection means algorithm ) OR ( parallel estimation path algorithm ))

Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms by Teoh, Chin Chuang

“…In cluster generating process, the developed BBSI algorithm was used to select the best band combination for generating cluster by using Iterative self- Organizing Data Analysis (ISODATA) technique. …”
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Determining the preprocessing clustering algorithm in radial basis function neural network by S.L. Ang, H.C. Ong, H.C. Law

“…The main objective in this study is to determine the better method to be used to find the centres in the Radial Basis Functional Link Nets for data classification. Three types of method used in this study to find the centres include random selections, K-means clustering algorithm and also K-median clustering algorithm. …”
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Integrating genetic algorithms and fuzzy c-means for anomaly detection by Chimphlee, Witcha, Abdullah, Abdul Hanan, Sap, Noor Md., Chimphlee, Siriporn, Srinoy, Surat

“…Genetic Algorithms (GA) to the problem of selection of optimized feature subsets to reduce the error caused by using land-selected features. …”
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Algorithmic approaches in model selection of the air passengers flows data by Ismail, Suzilah, Yusof, Norhayati, Tuan Muda, Tuan Zalizam

“…Algorithm is an important element in any problem solving situation.In statistical modelling strategy, the algorithm provides a step by step process in model building, model testing, choosing the ‘best’ model and even forecasting using the chosen model.Tacit knowledge has contributed to the existence of a huge variability in manual modelling process especially between expert and non-expert modellers.Many algorithms (automated model selection) have been developed to bridge the gap either through single or multiple equation modelling.This study aims to evaluate the forecasting performances of several selected algorithms on air passengers flow data based on Root Mean Square Error (RMSE) and Geometric Root Mean Square Error (GRMSE).The findings show that multiple models selection performed well in one and two step-ahead forecast but was outperformed by single model in three step-ahead forecasts.…”
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Determination of the Best Single Imputation Algorithm for Missing Rainfall Data Treatment by Saeed, Gamil Abdulraqeb Abdullah, Chuan, Zun Liang, Roslinazairimah, Zakaria, Wan Nur Syahidah, Wan Yusoff

“…The proposed algorithms use descriptive measures of the data, including arithmetric means, geometric means, harmonic means, medians and midranges. …”
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Determination of the best single imputation algorithm for missing rainfall data treatment by Gamil Abdulraqeb Abdullah Saeed, Chuan, Zun Liang, Roslinazairimah Zakaria, Wan Nur Syahidah Wan Yusoff, Mohd Zuki Salleh

“…The proposed algorithms use descriptive measures of the data, including arithmetric means, geometric means, harmonic means, medians and midranges. …”
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Discovering optimal clusters using firefly algorithm by Mohammed, Athraa Jasim, Yusof, Yuhanis, Husni, Husniza

“…Existing conventional clustering techniques require a pre-determined number of clusters, unluckily; missing information about real world problem makes it a hard challenge.A new orientation in data clustering is to automatically cluster a given set of items by identifying the appropriate number of clusters and the optimal centre for each cluster.In this paper, we present the WFA_selection algorithm that originates from weight-based firefly algorithm.The newly proposed WFA_selection merges selected clusters in order to produce a better quality of clusters.Experiments utilising the WFA and WFA_selection algorithms were conducted on the 20Newsgroups and Reuters-21578 benchmark dataset and the output were compared against bisect K-means and general stochastic clustering method (GSCM).Results demonstrate that the WFA_selection generates a more robust and compact clusters as compared to the WFA, bisect K-means and GSCM.…”
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Model selection approaches of water quality index data by Kamarudin, Nur Azulia, Ismail, Suzilah

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Extended multiple models selection algorithms based on iterative feasible generalized least squares (IFGLS) and expectation-maximization (EM) algorithm by Nur Azulia, Kamarudin

“…The empirical results for both algorithms performed well as compared to other models selection procedures, particularly using WQI data where the sample size is bigger and has good quality data. …”
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Efficient genetic partitioning-around-medoid algorithm for clustering by Garib, Sarmad Makki Mohammed

“…These algorithms mostly built upon the partitioning k-means clustering algorithm. …”
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MGR: An Information Theory Based Hierarchical Divisive Clustering Algorithm for Categorical Data by Hongwu, Qin, Ma, Xiuqin, Herawan, Tutut, Jasni, Mohamad Zain

“…This research proposes mean gain ratio (MGR), a new information theory based hierarchical divisive clustering algorithm for categorical data. …”
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Comparative analysis of K-Means and K-Medoids for clustering exam questions / Nurul Zafirah Mokhtar by Mokhtar, Nurul Zafirah

“…The studies had found that k-Medoids produced higher accuracy result with 0.11% higher than k-Means. As a conclusion, with this type of data, k-Medoids algorithm had shown higher accuracy result rather than k-Means.…”
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An observation of different clustering algorithms and clustering evaluation criteria for a feature selection based on linear discriminant analysis by Tie, K. H., A., Senawi, Chuan, Z. L.

“…Overall, the k-means outperforms the Gaussian mixture distribution in selecting smaller feature subsets. …”
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Gene selection for high dimensional data using k-means clustering algorithm and statistical approach by Ahmad, Farzana Kabir, Yusof, Yuhanis, Othman, Nor Hayati

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K-gen phishguard: an ensemble approach for phishing detection with k-means and genetic algorithm by Al-Hafiz, Ali Raheem, Jabir, Adnan J., Subramaniam, Shamala

“…This research presents a two-phase phishing detection system by employing unsupervised feature selection and supervised classification. In the first phase, the best set of features is identified by the Genetic algorithm and is utilised by the K-means clustering algorithm to divide the dataset into groups with similar traits. …”
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Magnetic resonance imaging sense reconstruction system using FPGA / Muhammad Faisal Siddiqui by Muhammad Faisal , Siddiqui

“…Parallel imaging is a robust method for accelerating the data acquisition in Magnetic Resonance Imaging (MRI). …”
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The new efficient and accurate attribute-oriented clustering algorithms for categorical data by Qin, Hongwu

“…This work firstly reveals the significance of attributes in categorical data clustering, and then investigates the limitations of algorithms MMR and G-ANMI respectively, and correspondingly proposes a new attribute-oriented hierarchical divisive clustering algorithm termed Mean Gain Ratio (MGR) and an improved genetic clustering algorithm termed Improved G-ANMI (IG-ANMI) for categorical data. …”
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Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares by Uraibi, Hassan Sami

“…The results on the model selection again signify that our proposed robust bootstrap model selection method is more robust than the classical bootstrap model selection.…”
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A combinatory algorithm of univariate and multivariate gene selection by Mahmoodian, Sayed Hamid, Marhaban, Mohammad Hamiruce, Abdul Rahim, Raha, Rosli, Rozita, Saripan, M. Iqbal

“…The results show that the mean of misclassification error of training samples in 100 iteration are almost equal in two algorithms but our algorithm have the better ability to classify independent samples.…”
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Implementation of machine learning algorithms for streamflow prediction of Dokan dam by Sarmad Dashti Latif, Mr.

“…The selected statistical indices are root mean square error (RMSE), mean absolute error (MAE), mean square error (MSE), correlation coefficient (R), and coefficient of determination (R2), Nash Sutcliffe Model Efficiency Coefficient (NSE), and the RMSE-observations standard deviation ratio (RSR). …”