Volume 3, Issue 4, 2013

By | September 4, 2018

Improving Performance of a Group of Classification Algorithms Using Resampling and Feature Selection

Mehdi Naseriparsa, Amir-masoud Bidgoli, Touraj Varaee
Islamic Azad University, Tehran North Branch, Department Of Computer Engineering, Iran.

Abstract— in recent years the importance of finding a meaningful pattern from huge datasets has become more challenging. Data miners try to adopt innovative methods to face this problem by applying feature selection methods. In this paper we propose a new hybrid method in which we use a combination of resampling, filtering the sample domain and wrapper subset evaluation method with genetic search to reduce dimensions of Lung-Cancer dataset that we received from UCI Repository of Machine Learning databases. Finally, we apply some well- known classification algorithms (Naïve Bayes, Logistic, Multilayer Perceptron, Best First Decision Tree and JRIP) to the resulting dataset and compare the results and prediction rates before and after the application of our feature selection method on that dataset. The results show a substantial progress in the average performance of five classification algorithms simultaneously and the classification error for these classifiers decreases considerably. The experiments also show that this method outperforms other feature selection methods with a lower cost.

Keywords-Feature Selection; Reliable Features; Lung-Cancer; Classification Algorithms.

Simulation of Improved Academic Achievement for a Mathematical Topic Using Neural Networks Modeling

Saeed A. Al-Ghamdi, Hassan M. H. Mustafa
Faculty of Engineering, Al-Baha University, Al-Baha, Kingdom of Saudi Arabia.
Abdel Aziz M. Al-Bassiouni, Telecommunication & Technology Company, Cairo, Egypt.
Ayoub Al-Hamadi, Institute for Information and Communication Technology, Otto- von-Guericke-University Magdeburg, Germany.

Abstract— This paper is inspired by the simulation of Artificial Neural Networks (ANNs) applied recently for evaluation of phonics methodology to teach the children “how to read?” Nevertheless, in this paper, a novel approach is presented aiming to improve the academic achievement in learning children as an adopted mathematical topic namely long division problem. That’s by comparative study of practical application results at educational field (a children classroom); for two computer aided learning (CAL) packages versus classical learning (case study). Presented study highly recommends the novel application of interdisciplinary teaching trends as a measure for learning performance evaluation. It is based on ANNs modeling, memory association, behaviorism, and individual’s learning styles. Interestingly, observed and obtained practical findings after the field application, proved the superiority of the package associated with teacher’s voice over both without voice, and classical learning / teaching as well.

Keywords-Artificial Neural Networks; Learning Performance Evaluation; Computer Aided Learning; Long Division Process; Associative Memory.

A Conceptual Nigeria Stock Exchange Prediction: Implementation Using Support Vector Machines-SMO Model

Abubakar S. Magaji, Faculty of Science, Kaduna State University, Nigeria.
Victor Onomza Waziri, Audu Isah, Adeboye K.R.Federal University of Technology Minna-Nigeria.

Abstract— This paper is a continuation of our research work on the Nigerian Stock Exchange (NSE) market uncertainties, In our first paper (Magaji et al, 2013) we presented the Naive Bayes algorithm as a tool for predicting the Nigerian Stock Exchange Market; subsequently we used the same transformed data of the NSE and explored the implementation of the Support Vector Machine algorithm on the WEKA platform, and results obtained, made us to also conclude that the Support Vector Machine-SOM is another algorithm that provides an avenue for predicting the Nigerian Stock Exchange.

Keywords- Nigerian Stock Market; Prediction; Data Mining; Machine Learning; Support Vector Machine.

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