Search Results - (( based optimization method algorithm ) OR ( variables classification problem 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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Application of Optimization Methods for Solving Clustering and Classification Problems by Shabanzadeh, Parvaneh

“…The focus of this thesis is on solvingclustering and classification problems. Specifically, we will focus on new optimization methods for solving clustering and classification problems. …”
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A genetic algorithm based fuzzy inference system for pattern classification and rule extraction by Wong S.Y., Yap K.S., Li X.

“…Therefore, our proposed GA-FIS method will first define the membership functions with logical interpretation which is amendable by domain experts to human understanding, and then genetic algorithm serves as an optimization tool to construct the best combination of rules in fuzzy inference system that can achieve higher classification accuracy and gain better interpretability. …”

Information Theoretic-based Feature Selection for Machine Learning by Muhammad Aliyu, Sulaiman

“…The problem with many existing feature selections that evaluate features based on mutual information is that they are designed to handles classification tasks only. …”
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Hybrid conjugate gradient using exact line search in photovoltaic system / Muhammad Ariiq Iqbaal Azizul Firdaus by Azizul Firdaus, Muhammad Ariiq Iqbaal

“…The conjugate gradient (CG) method is widely recognised as one of the most efficient numerical methods for solving unconstrained optimization problems. …”
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Modeling forest fires risk using spatial decision tree by Yaakob, Razali, Mustapha, Norwati, Nuruddin, Ahmad Ainuddin, Sitanggang, Imas Sukaesih

“…In addition, criteria evaluation and weighting method are most applied to evaluate the small problem containing few criteria. This paper presents our initial work in developing a spatial decision tree using the spatial ID3 algorithm and Spatial Join Index applied in the SCART (Spatial Classification and Regression Trees) algorithm. …”
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Classification for large number of variables with two imbalanced groups by Ahmad Hakiim, Jamaluddin

“…This study proposed two algorithms of classification namely Algorithm 1 and Algorithm 2 which combine resampling, variable extraction, and classification procedure. …”
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Statistical band selection for descriptors of MBSE and MFCC-based features for accent classification of Malaysian English / Yusnita M. A. ...[et al.] by M. A., Yusnita, M. P., Paulraj, Yaacob, Sazali, A. B., Shahriman, Mokhtar, Nor Fadzilah

“…This paper proposes an efficient way of analyzing the ethnical accent using statistical knowledge of log-energies of fourier transformed derived mel-filter banks. A simple algorithm to select bands so called statistical band selection (SBS) method using smallest variances within class scores was developed to optimize the presentation of speech features. …”
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Feature selection and model selection algorithm using incremental mixed variable ant colony optimization for support vector machine classifier by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…In order to enhance SVM performance, these problems must be solved simultaneously because error produced from the feature subset selection phase will affect the values of the SVM parameters and resulted in low classification accuracy.Most approaches related with solving SVM model selection problem will discretize the continuous value of SVM parameters which will influence its performance.Incremental Mixed Variable Ant Colony Optimization (IACOMV) has the ability to solve SVM model selection problem without discretising the continuous values and simultaneously solve the two problems.This paper presents an algorithm that integrates IACOMV and SVM.Ten datasets from UCI were used to evaluate the performance of the proposed algorithm.Results showed that the proposed algorithm can enhance the classification accuracy with small number of features.…”
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Hybrid ACO and SVM algorithm for pattern classification by Alwan, Hiba Basim

“…Ant Colony Optimization (ACO) is a metaheuristic algorithm that can be used to solve a variety of combinatorial optimization problems. …”
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Study on numerical solution of a variable order fractional differential equation based on symmetric algorithm by Liu, Jingrui, Pan, Dongyang

“…A fully symmetric classification of the boundary value problem for a class of fractional differential equations with variable sequences is determined by using a fully symmetric differential sequence sorting algorithm. …”
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Mixed-variable ant colony optimisation algorithm for feature subset selection and tuning support vector machine parameter by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…This paper presents a hybrid classification algorithm, ACOMV-SVM which is based on ant colony and support vector machine.A new direction for ant colony optimisation is to optimise mixed (discrete and continuous) variables.The optimised variables are then feed into selecting feature subset and tuning its parameters are two main problems of SVM.Most approaches related to tuning support vector machine parameters will discretise the continuous value of the parameters which will give a negative effect on the performance. …”
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A New Quadratic Binary Harris Hawk Optimization For Feature Selection by Abdullah, Abdul Rahim, Too, Jing Wei, Mohd Saad, Norhashimah

“…However, the original HHO is developed to solve the continuous optimization problems, but not to the problems with binary variables. …”
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Feature Ranking Techniques For 3D ATS Drug Molecular Structure Identification by Saw, Yee Ching

“…Six feature ranking techniques were used: Information Gain (IG), Gain Ratio (GR), Symmetrical Uncertainty (SU), Support vector machine based recursive feature elimination (SVM-RFE), and Variable Importance based random forest (VI-RF). …”
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Integrated ACOR/IACOMV-R-SVM Algorithm by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…A direction for ACO is to optimize continuous and mixed (discrete and continuous) variables in solving problems with various types of data. …”
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Gas Identi cation by Using a Cluster-k-Nearest-Neighbor by Brahim Belhaouari, samir

“…We find 98.7% of accuracy in the classification of 6 different types of Gas by using K-means cluster algorithm and we find almost the same by using the new clustering algorithm.…”
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Solving SVM model selection problem using ACOR and IACOR by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…In applying ACO for optimizing SVM parameters which are continuous variables, there is a need to discretize the continuously value into discrete values.This discretize process would result in loss of some information and hence affect the classification accuracy.In order to enhance SVM performance and solving the discretization problem, this study proposes two algorithms to optimize SVM parameters using Continuous ACO (ACOR) and Incremental Continuous Ant Colony Optimization (IACOR) without the need to discretize continuous value for SVM parameters.Eight datasets from UCI were used to evaluate the credibility of the proposed integrated algorithm in terms of classification accuracy and size of features subset.Promising results were obtained when compared to grid search technique, GA with feature chromosome-SVM, PSO-SVM, and GA-SVM. …”
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Framework of Meta-Heuristic Variable Length Searching for Feature Selection in High-Dimensional Data by Qadir Saraf, Tara Othman, Fuad, Norfaiza, Md Taujuddin, Nik Shahidah Afifi

“…The literature contains various meta-heuristic algorithms with variable length searching. All of them enable searching in high dimensional problems. …”
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Framework of Meta-Heuristic Variable Length Searching for Feature Selection in High-Dimensional Data by Qadir Saraf, Tara Othman, Fuad, Norfaiza, Md Taujuddin, Nik Shahidah Afifi

“…The literature contains various meta-heuristic algorithms with variable length searching. All of them enable searching in high dimensional problems. …”
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Framework of Meta-Heuristic Variable Length Searching for Feature Selection in High-Dimensional Data by Qadir Sara, Tara Othman, Fuad, Norfaiza, Md Taujuddin, Nik Shahidah Afifi

“…The literature contains various meta-heuristic algorithms with variable length searching. All of them enable searching in high dimensional problems. …”
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