Search Results - (( program based ((data algorithm) OR (tree algorithm)) ) OR ( java implication 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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Performance evaluation of inverted files, B-Tree and B+ Tree indexing algorithm on Malay text / Suhanah Rosnan by Rosnan, Suhanah

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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

“…We improved the ID3 decision tree algorithm such that it can be utilized on spatial data in order to develop a classification model for hotspots occurrence. …”
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Building customer churn prediction models in Indonesian telecommunication company using decision tree algorithm by Ramadhanti,, Darin, Larasati, Aisyah, Muid, Abdul, Mohamad, Effendi

“…Preventive efforts are needed by predicting the value of churn in the future. This study uses data mining techniques with decision tree algorithms to predict customer churn in one of Indonesian Telecommunication companies. …”
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Predicting Student Performance in Object Oriented Programming Using Decision Tree : A Case at Kolej Poly-Tech Mara, Kuantan by Mohd Hanis, Rani, Abdullah, Embong

“…Using 10-fold cross validation for each algorithm, it was found that decision tree was the best algorithm with 83.6944% correctness. …”
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Decision Tree: Customer churn analysis for a loyalty program using data mining algorithm by Lee, Angela Siew Hoong *, Zuraini Zainol, Ng, Claudia, Chan, Khin Whai

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Decision tree method for fault causes classification based on RMS-DWT analysis in 275 kV transmission lines network by Asman, Saidatul Habsah, Ab Aziz, Nur Fadilah, Ungku Amirulddin Al Amin, Ungku Anisa, Ab Kadir, Mohd Zainal Abidin

“…The proposed algorithm is based upon the root mean square (RMS) current duration, voltage dip, and discrete wavelet transform (DWT) measured at the sending end of a line and the decision tree method, a commonly accessible measurable method. …”
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An efficient and effective case classification method based on slicing by Shiba, Omar A. A., Sulaiman, Md. Nasir, Mamat, Ali, Ahmad, Fatimah

“…The algorithms are: Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5). …”
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Prime-based method for interactive mining of frequent patterns by Nadimi-Shahraki, Mohammad-Hossein

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A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem by Mohd Pozi, Muhammad Syafiq

“…There are two methods in dealing with imbalanced classification problem, which are based on data or algorithmic level. Data level based methods are meant to solve the imbalanced classification problem based on the idea of making both classes equal in number. …”
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Decision tree method for fault causes classification based on rms-dwt analysis in 275 kv transmission lines network by Asman S.H., Aziz N.F.A., Amirulddin U.A.U., Kadir M.Z.A.A.

“…The proposed algorithm is based upon the root mean square (RMS) current duration, voltage dip, and discrete wavelet transform (DWT) measured at the sending end of a line and the decision tree method, a commonly accessible measurable method. …”

Correlation analysis and predictive performance based on KNN and decision tree with augmented reality for nuclear primary cooling process / Ahmad Azhari Mohamad Nor by Mohamad Nor, Ahmad Azhari

“…Subsequently, predictive models employing k-nearest neighbour and decision tree algorithms are constructed and evaluated based on accuracy, precision, and recall metrics. …”
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Image classification using two dimensional wavelet coefficients with parallel computing by Ong, Yew Fai

“…This research algorithm demonstrated a very promising result with Support Vector Machines, this algorithm produces a 90% of accuracies whereas the decision tree algorithm gets 100% accuracies. …”
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Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining. by Saeed, Walid

“…The accuracy for rules and classification resulted from the TIP method are compared with other methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) from Rough Set, Genetic Algorithm (GA), Johnson reducer, HoltelR method, Multiple Regression (MR), Neural Network (NN), Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5); all other classifiers that are mostly used in the classification tasks. …”
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Enhancing understanding of programming concepts through physical games by Raja Yusof, Raja Jamilah, Habib, Ahsan

“…The activities were conducted involving first and fourth year undergraduate students and Master students in Programming 1 (31 students), Data Structure (6 students), Analysis of Algorithm (12 students) and Advanced Algorithm (22 students) courses respectively. …”
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A cluster-based hybrid replica control protocol for high availability in data grid by Mabni, Zulaile

“…This research has contributed a dynamic cluster-based hybrid replica control protocol which proposed a clustering algorithm to determine the number of clusters, a mechanism for dynamic participation of nodes in the network, and a replica placement algorithm that produces low communication cost and high data availability as compared to DH and DDG protocols. …”
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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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Prediction of stroke disease using machine learning techniques / Syarifah Adilah Mohamed Yusoff ... [et al.] by Mohamed Yusoff, Syarifah Adilah, Warris, Saiful Nizam, Abu Bakar, Mohd Saifulnizam, Kadar, Rozita

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Classification models for higher learning scholarship award decisions by Wirawati Dewi Ahmad, Azuraliza Abu Bakar

“…Five algorithms were employed to develop a classification model in determining the award of the scholarship, namely J48, SVM, NB, ANN and RT algorithms. …”
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Enhanced dynamic security assessment for power system under normal and fake tripping contingencies. by A. Salih, Qusay

“…The hybrid logistic model tree (hybrid LMT) approach proposed in this study combines the symmetrical uncertainties (SU) algorithm and the logistic model tree (LMT) algorithm. …”
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