Search Results - (( feature selection tree algorithm ) OR ( java application based algorithm ))

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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A Hybrid Gini PSO-SVM Feature Selection: An Empirical Study of Population Sizes on Different Classifier by Noormadinah Allias, Megat NorulAzmi Megat Mohamed Noor, Mohd. Nazri Ismail, Kim de Silva, (UniKL MIIT)

“…Deriving from previous experiments, we extended our work by investigating the effect of population sizes from our proposed method of feature selection on different learning classifier algorithms using Random Forest, Voting, Decision Tree, Support Vector Machine and Stacking. …”

An extended ID3 decision tree algorithm for spatial data by Sitanggang, Imas Sukaesih, Yaakob, Razali, Mustapha, Norwati, Nuruddin, Ahmad Ainuddin

“…The objective of this paper is to propose a new spatial decision tree algorithm based on the ID3 algorithm for discrete features represented in points, lines and polygons. …”
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Improved random forest for feature selection in writer identification by Sukor, Nooraziera Akmal

“…An algorithm and framework of Improved Random Forest (IRF) tree was applied for feature selection process. …”
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Tree-based contrast subspace mining method by Florence Sia Fui Sze

“…The tree-based method begins with feature selection phase which finds relevant features and followed by contrast subspace search phase to search contrast subspaces from the relevant features, accordance to the tree-based likelihood contrast scoring function. …”
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Exploring important factors in predicting heart disease based on ensembleextra feature selection approach by Howida Abubaker, Farkhana Muchtar, Alif Ridzuan Khairuddin, Ahmad Najmi Amerhaider Nuar, Zuriahati Mohd Yunos, Carolyn Salimun

“…The results show that the feature selection technique achieves outstanding classification accuracy, precision, and recall, with an impressive 97% accuracy when used with the Extra Tree classifier algorithm. …”
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Classification of basal stem rot disease in oil palm using dielectric spectroscopy by Al-Khaled, Al-Fadhl Yahya Khaled

“…First, features selection algorithms (genetic algorithm (GA), random forest (RF), and support vector machine-feature selection (SVM-FS)) were used to select the most significant frequencies. …”
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Real-time power system dynamic security assessment based on advanced feature selection for decision tree classifiers by Al-Gubri, Qusay, Mohd Ariff, Mohd Aifaa

“…This paper proposed a novel algorithm based on advanced feature selection technique for decision tree (DT) classifier to assess the dynamic security in power system. …”
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Performance evaluation of intrusion detection system using selected features and machine learning classifiers by Raja Mahmood, Raja Azlina, Abdi, AmirHossien, Hussin, Masnida

“…The two sets of selected features are based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) approach respectively. …”
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Design Of Feature Selection Methods For Hand Movement Classification Based On Electromyography Signals by Too, Jing Wei

“…For this purpose, the feature selection (FS) method is applied to evaluate the best feature subset from a large available feature set. …”
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Classification model for hotspot occurrences using spatial decision tree algorithm by Sitanggang, Imas Sukaesih, Yaakob, Razali, Mustapha, Norwati, Nuruddin, Ahmad Ainuddin

“…The ID3 algorithm which is originally designed for a non-spatial dataset has been improved to construct a spatial decision tree from a spatial dataset containing discrete features (points, lines and polygons). …”
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Enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree by Arowolo, Micheal Olaolu, Adebiyi, Marion Olubunmi, Adebiyi, Ayodele Ariyo

“…The proposed algorithm is used to fetch relevant features based from the high-dimensional input feature space. …”
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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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E2IDS: an enhanced intelligent intrusion detection system based on decision tree algorithm by Bouke, Mohamed Aly, Abdullah, Azizol, ALshatebi, Sameer Hamoud, Abdullah, Mohd Taufik

“…The model design is Decision Tree (DT) algorithm-based, with an approach to data balancing since the data set used is highly unbalanced and one more approach for feature selection. …”
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Prediction of employee promotion using hybrid sampling method with machine learning architecture / Shahidan Shafie, Soek Peng Ooi and Khai Wah Khaw by Shafie, Shahidan, Soek, Peng Ooi, Khai, Wah Khaw

“…The first 5, 8, and 12 features are selected based on the RFI-ECT to train the machine learning algorithms. …”
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Intersection Features For Android Botnet Classification by Ismail, Najiahtul Syafiqah, Yusof, Robiah, Saad, Halizah, Abdollah, Mohd Faizal, Yusof, Robiah

“…The results showed that Decision Tree with Chi-Square feature selection achieved the highest detection accuracy of 98.6% which was higher than other classifiers.…”
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Evaluation of data mining models for predicting concrete strength by Wong, Chuan Ming

“…These models are all evaluated with hyperparameter tuning and different feature selection techniques. The feature selection techniques included are (i) Principle Component Analysis, (ii) Boruta and (iii) LASSO. …”
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Prediction of Heart Disease Risk Using Machine Learning with Correlation-based Feature Selection and Optimization Techniques by Reddy, K.V.V., Elamvazuthi, I., Aziz, A.A., Paramasivam, S., Chua, H.N., Pranavanand, S.

“…Finally, performed a comparative analysis with and without feature selection. The Optimizable k-Nearest Neighbors algorithm achieved an utmost accuracy of 95.04, area under the ROC curve of 0.99 on the Correlation-based Feature Selection optimal set, and that of 90.34, 0.96 respectively, on full features. …”
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A class skew-insensitive ACO-based decision tree algorithm for imbalanced data sets by Mohd Razali, Muhamad Hasbullah, Saian, Rizauddin, Yap, Bee Wah, Ku-Mahamud, Ku Ruhana

“…Ant-tree-miner (ATM) has an advantage over the conventional decision tree algorithm in terms of feature selection. …”
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Behavioural features for mushroom classification by Ismail, Shuhaida, Zainal, Amy Rosshaida, Mustapha, Aida

“…The Principal Component Analysis (PCA) algorithm is used for selecting the best features for the classification experiment using Decision Tree (DT) algorithm. …”
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