Search Results - (( variable selection based algorithm ) OR ( using classification modeling algorithm ))

Formulating new enhanced pattern classification algorithms based on ACO-SVM by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…ACO originally deals with discrete optimization problem.In applying ACO for solving SVM model selection problem which are continuous variables, there is a need to discretize the continuously value into discrete values.This discretization process would result in loss of some information and hence affects the classification accuracy and seeking time.In this algorithm we propose to solve SVM model selection problem using IACOR without the need to discretize continuous value for SVM.The second algorithm aims to simultaneously solve SVM model selection problem and selects a small number of features.SVM model selection and selection of suitable and small number of feature subsets must occur simultaneously because error produced from the feature subset selection phase will affect the values of SVM model selection and result in low classification accuracy.In this second algorithm we propose the use of IACOMV to simultaneously solve SVM model selection problem and features subset selection.Ten benchmark datasets were used to evaluate the proposed algorithms.Results showed that the proposed algorithms can enhance the classification accuracy with small size of features subset.…”
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A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification by Talpur, N., Abdulkadir, S.J., Hasan, M.H., Alhussian, H., Alwadain, A.

“…The performance of the proposed SCSO algorithm was compared with six state-of-the-art and recent wrapper-based optimization algorithms using the validation metrics of classification accuracy, optimum feature size, and computational cost in seconds. …”
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Gene Selection For Cancer Classification Based On Xgboost Classifier by Teo, Voon Chuan

“…Due to this situation, development of the gene selection method has become more important in obtain useful information for cancer classification, and diagnoses for other diseases. …”
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Using fuzzy association rule mining in cancer classification by Mahmoodian, Sayed Hamid, Marhaban, Mohammad Hamiruce, Abdul Rahim, Raha, Rosli, Rozita, Saripan, M. Iqbal

“…In addition, creating a fuzzy classifier with high performance in classification that uses a subset of significant genes which have been selected by different types of gene selection methods is another goal of this study. …”
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Extremal region detection and selection with fuzzy encoding for food recognition by Razali @ Ghazali, Mohd Norhisham

“…The second algorithm reduces the quantity of interest regions by using the Extremal Region Selection (ERS) algorithm. …”
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Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad by Ahmad, Khairul Adilah

“…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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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

“…ML models were constructed using 302 patients and 54 input variables from the Malaysian National Cardiovascular Disease Database. …”
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Robust diagnostics and variable selection procedure based on modified reweighted fast consistent and high breakdown estimator for high dimensional data by Baba, Ishaq Abdullahi

“…Sure screening-based correlation methods are popular tools used to select the most significant variables in the true model in sparse and high dimensional analysis. …”
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Decision tree and rule-based classification for predicting online purchase behavior in Malaysia / Maslina Abdul Aziz, Nurul Ain Mustakim and Shuzlina Abdul Rahman by Abdul Aziz, Maslina, Mustakim, Nurul Ain, Abdul Rahman, Shuzlina

“…The current study contributes to the literature by highlighting decision tree and rule-based classification models as very useful in the Malaysian e-commerce context. …”
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Improving hand written digit recognition using hybrid feature selection algorithm by Wong, Khye Mun

“…While mRMR was capable of identifying a subset of features that were highly relevant to the targeted classification variable, it still carry the weakness of capturing redundant features along with the algorithm. …”
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Green building valuation based on machine learning algorithms / Thuraiya Mohd ... [et al.] by Mohd, Thuraiya, Jamil, Syafiqah, Masrom, Suraya, Ab Rahim, Norbaya

“…This experiment used five common machine learning algorithms namely 1) Linear Regressor, 2) Decision Tree Regressor, 3) Random Forest Regressor, 4) Ridge Regressor and 5) Lasso Regressor tested on a real estate data-set of covering Kuala Lumpur District, Malaysia. 3 set of experiments was conducted based on the different feature selections and purposes The results show that the implementation of 16 variables based on Experiment 2 has given a promising effect on the model compare the other experiment, and the Random Forest Regressor by using the Split approach for training and validating data-set outperformed other algorithms compared to Cross-Validation approach. …”
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The Identification of High Potential Archers Based on Fitness and Motor Ability Variables: A Support Vector Machine Approach by Zahari, Taha, Rabiu Muazu, Musa, Anwar, P. P. Abdul Majeed, Muhammad Muaz, Alim, Mohamad Razali, Abdullah

“…Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the performance variables tested. …”
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Finding an effective classification technique to develop a software team composition model by Gilal, A.R., Jaafar, J., Capretz, L.F., Omar, M., Basri, S., Aziz, I.A.

“…Higher prediction accuracy and reduced pattern complexity were the 2 parameters for selecting the effective technique. Based on the results, the Johnson algorithm (JA) of RST appeared to be an effective technique for a team composition model. …”
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Finding an effective classification technique to develop a software team composition model by Gilal, A.R., Jaafar, J., Capretz, L.F., Omar, M., Basri, S., Aziz, I.A.

“…Higher prediction accuracy and reduced pattern complexity were the 2 parameters for selecting the effective technique. Based on the results, the Johnson algorithm (JA) of RST appeared to be an effective technique for a team composition model. …”
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Assessment of predictive models for chlorophyll-a concentration of a tropical lake. by Malek, S., Syed Ahmad, S. M., Singh, S. K., Milow, P., Salleh, A.

“…HEA model used parameters selected using genetic algorithm (GA). …”
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Assessment of predictive models for chlorophyll-a concentration of a tropical lake. by Syed Ahmad Abdul Rahman, Sharifah Mumtazah, Malek, Sorayya, Kashmir Singh, Sarinder Kaur, Milow, Pozi, Salleh, Aishah

“…HEA model used parameters selected using genetic algorithm (GA). …”
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A hybrid serendipity social recommender model / Ahmad Subhi Zolkafly and Rahayu Ahmad by Zolkafly, Ahmad Subhi, Ahmad, Rahayu

“…In the next stage, the classification model is used to classify the data into subclasses by using a deep learning algorithm. …”
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Classification and quantification of palm oil adulteration via portable NIR spectroscopy by Basri, Katrul Nadia, Hussain, Mutia Nurulhusna, Bakar, Jamilah, Sharif, Zaiton, Abdul Khir, Mohd Fared, Zoolfakar, Ahmad Sabirin

“…In order to remove the uninformative variables, variable selection using cumulative adaptive reweighted sampling (CARS) has been performed. …”
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Finding an effective classification technique to develop a software team composition model by Gilal, Abdul Rehman, Jaafar, Jafreezal, Capretz, Luiz Fernando, Omar, Mazni, Basri, Shuib, Abdul Aziz, Izzatdin

“…Ineffective software team composition has become recognized as a prominent aspect of software project failures.Reports from results extracted from different theoretical personality models have produced contradicting fits, validity challenges, and missing guidance during software development personnel selection.It is also believed that the technique/s used while developing a model can impact the overall results.Thus, this study aims to: 1) discover an effective classification technique to solve the problem, and 2) develop a model for composition of the software development team.The model developed was composed of three predictors: team role, personality types, and gender variables; it also contained one outcome: team performance variable.The techniques used for model development were logistic regression, decision tree, and Rough Sets Theory (RST).Higher prediction accuracy and reduced patte rn complexity were the two parameters forselecting the effective technique.Based on the results, the Johnson Algorithm (JA) of RST appeared to be an effective technique for a team composition model.The study has proposed a set of 24 decision rules for finding effective team members.These rules involve gender classification to highlight the appropriate personality profile for software developers.In the end, this study concludes that selecting an appropriate classification technique is one of the most important factors in developing effective models.…”
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Classification and quantification of palm oil adulteration via portable NIR spectroscopy by Basri, Katrul Nadia, Hussain, Mutia Nurulhusna, Bakar, Jamilah, Sharif, Zaiton and, Zoolfakar, Ahmad Sabirin, Mohd Fared Abdul Khir

“…In order to remove the uninformative variables, variable selection using cumulative adaptive reweighted sampling (CARS) has been performed. …”