Search Results - (( subset selection methods algorithm ) OR ( using estimation method algorithm ))

Short-term electricity price forecasting in deregulated electricity market based on enhanced artificial intelligence techniques / Alireza Pourdaryaei by Alireza , Pourdaryaei

“…In the developed method of multi-objective feature determination, MOBBSA is used to search within different combinations of input variables and to select the non-dominated feature subsets. …”
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Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli by Seyed Hamidreza , Aghay Kaboli

“…In the developed feature selection approach, multi-objective binary-valued backtracking search algorithm (MOBBSA) is used as an efficient evolutionary search algorithm to search within different combinations of input variables and selects the non-dominated feature subsets, which minimize simultaneously both the estimation error and the number of features. …”
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New entropy-based method for gene selection by Mahmoodian, Sayed Hamid, Marhaban, Mohammad Hamiruce, Abdul Rahim, Raha, Rosli, Rozita, Saripan, M. Iqbal

“…In this paper, we use Shannon theorem and penalized logistic regression (PLR) as a probability estimator to present a new algorithm for dimension reduction and collect a subset of representative genes of gene expression profile. …”
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Information Theoretic-based Feature Selection for Machine Learning by Muhammad Aliyu, Sulaiman

“…Also, additional experiments to compare the relative performance of the IFS with five related feature selection algorithms were carried out using natural domain datasets. …”
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Tree-based contrast subspace mining method by Florence Sia Fui Sze

“…Then, the tree-based method, the genetic algorithm based parameter values identification of tree-based method, and followed by the genetic algorithm based tree-based method, for numerical data sets are developed and evaluated. …”
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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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Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data by Oyebayo, Olaniran Ridwan

“…Random Forests (RF) are ensemble of trees methods widely used for data prediction, interpretation and variable selection purposes. …”
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Time series modeling of water level at Sulaiman Station, Klang River, Malaysia by Galavi, Hadi

“…Using the cross validation method the best training subset is selected to train the ANFIS model based on that dataset. …”
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Class binarization with self-adaptive algorithm to improve human activity recognition by Zainudin, Muhammad Noorazlan Shah

“…In order to estimate the quality of ‘pruned’ features, self-adaptive DE algorithm is proposed. …”
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Power prediction using the wind turbine power curve and data-driven approaches / Ehsan Taslimi Renani by Ehsan Taslimi , Renani

“…In this method, firstly, Weibull density function is utilized to model the wind speed and then several methods are applied to estimate the parameters of the wind speed distribution. …”
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Aco-based feature selection algorithm for classification by Al-mazini, Hassan Fouad Abbas

“…However, the MGCACO algorithm has three main drawbacks in producing a features subset because of its clustering method, parameter sensitivity, and the final subset determination. …”
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A direct ensemble classifier for learning imbalanced multiclass data by Samry @ Mohd Shamrie Sainin

“…The DECIML with feature selection is also evaluated and compared with methods named CFsSubsetEval and Filteredsubseteval. …”
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Prediction Of Petroleum Reservoir Properties Using Nonlinear Feature Selection And Ensembles Of Computational Intelligence Techniques by Anifowose, Fatai Adesina

“…A thorough analysis of the comparative results showed that our proposed methods and algorithms outperformed the benchmarks. …”
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Formulating new enhanced pattern classification algorithms based on ACO-SVM by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

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Case Slicing Technique for Feature Selection by A. Shiba, Omar A.

“…CST was compared to other selected classification methods based on feature subset selection such as Induction of Decision Tree Algorithm (ID3), Base Learning Algorithm K-Nearest Nighbour Algorithm (k-NN) and NaYve Bay~sA lgorithm (NB). …”
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An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

“…Experiments demonstrate that ensemble classifier learning method produces better accuracy mining data streams and selecting subset of relevant features comparing other single classifiers. …”
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Towards a better feature subset selection approach by Shiba, Omar A. A.

“…The selection of the optimal features subset and the classification has become an important issue in the data mining field.We propose a feature selection scheme based on slicing technique which was originally proposed for programming languages.The proposed approach called Case Slicing Technique (CST).Slicing means that we are interested in automatically obtaining that portion 'features' of the case responsible for specific parts of the solution of the case at hand.We show that our goal should be to eliminate the number of features by removing irrelevant once.Choosing a subset of the features may increase accuracy and reduce complexity of the acquired knowledge.Our experimental results indicate that the performance of CST as a method of feature subset selection is better than the performance of the other approaches which are RELIEF with Base Learning Algorithm (C4.5), RELIEF with K-Nearest Neighbour (K-NN), RELIEF with Induction of Decision Tree Algorithm (ID3) and RELIEF with Naïve Bayes (NB), which are mostly used in the feature selection task.…”
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Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi by Atefi, Kayvan

“…Experiments demonstrate and prove that the proposed EBPSO method produces better accuracy mining data and selecting subset of relevant features comparing other algorithms. …”
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An empirical study of double-bridge search move on subset feature selection search of bees algorithm by Al-dawoodi, Aras Ghazi Mohammed, Mahmuddin, Massudi

“…This creates a heavy computational time, and in the same time could affect the overall accuracy subset selection.To rectify this issue, a double-bridge move proposed and benchmark dataset have been used to determine the performance of the proposed method. …”
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Ant colony optimization based subset feature selection in speech processing: Constructing graphs with degree sequences by Rajesvary Rajoo R., Salam R.A.