An experimental investigation of the impact of aggregation on the performance of data mining with logistic regression

Fadlalla, Adam

doi:10.1016/j.im.2004.04.005

Cited by 18 publications

(5 citation statements)

References 32 publications

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“…The data were used without transformation.Partition the data into a training set for developing the model and a testing set for unbiased model assessment. It is well accepted that, for unbiased results, models should be tested on a different data set than the set on which they are developed (Bleeker et al , ; Terrin et al , ; Fadlalla, ). A practical way to obtain an unbiased model assessment is to divide the data set into a training set and a testing set.…”

Section: Methodsmentioning

confidence: 99%

Predicting Next Trading Day Closing Price of Qatar Exchange Index Using Technical Indicators and Artificial Neural Networks

Fadlalla

Amani

2014

Intell. Sys. Acc. Fin. Mgmt.

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Accurate prediction of stock market price is of great importance to many stakeholders. Artificial neural networks (ANNs) have shown robust capability in predicting stock price return, future stock price and the direction of stock market movement. The major aim of this study is to predict the next trading day closing price of the Qatar Exchange (QE) Index using historical data from 3 January 2010 to 31 December 2012. A multilayer perceptron ANN architecture was used as a prediction model with 10 market technical indicators as input variables. The experimental results indicate that ANNs are an effective modelling technique for predicting the QE Index with high accuracy, outperforming the well-established autoregressive integrated moving average models. To the best of our knowledge, this is the first attempt to use ANNs to predict the QE Index, and its performance results are comparable to, and sometimes better than, many stock market predictions reported in the literature. The ANN model also revealed that the weighted and simple moving averages are the most important technical indicators in predicting the QE Index, and the accumulation/distribution oscillator is the least important such indicator. The analysis results also indicated that the ANNs are resilient to stock market volatility.

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Section: Methodsmentioning

confidence: 99%

Predicting Next Trading Day Closing Price of Qatar Exchange Index Using Technical Indicators and Artificial Neural Networks

Fadlalla

Amani

2014

Intell. Sys. Acc. Fin. Mgmt.

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“…Logistic regression analysis applies maximum likelihood estimation after transforming the dependent variable (graduation) into a Logit variable (the natural log of the odds of the dependent response occurring or not); therefore, logistic regression will estimate the odds that an existing student graduated or not graduated (Hosmer & Lemeshow, 2000). Logistic regression has gained reputation in data mining techniques because it does not require assumptions such as linearity, normality, and homoscedasticity compared to Ordinary Least Squares (OLS) regression (Fadlalla, 2005). The binary outcome for the target variable graduation (graduated (1)/not graduated (0)) can be represented as the conditional mean of Y given X = x, represented as E(Y|x), where E(Y|x) is the expected value of the target variable graduation for a given value of predictor.…”

Section: Logistic Regressionmentioning

confidence: 99%

Comparing Data Mining Models in Academic Analytics

Raju

Schumacker

2016

International Journal of Knowledge-Based Organizations

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The goal of this research study was to compare data mining techniques in predicting student graduation. The data included demographics, high school, ACT profile, and college indicators from 1995-2005 for first-time, full-time freshman students with a six year graduation timeline for a flagship university in the south east United States. The results indicated no difference in misclassification rates between logistic regression, decision tree, neural network, and random forest models. The results from the study suggest that institutional researchers should build and compare different data mining models and choose the best one based on its advantages. The results can be used to predict students at risk and help these students graduate.

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“…These features are classified by the two classifiers, Neural Networks (NN) and SVM were used to classify eight upper limb motions and presented the highest accuracy rate was by WP (97.7%) [15].Different decision tree methods have become the part of research in the classification of EMG signal by extracting different features. Researchers have used different machine learning algorithms for classification of EMG signal [16,17].…”

Section: Introductionmentioning

confidence: 99%

Wavelet Based Machine Learning Technique to Classify the Different Shoulder Movement of Upper Limb Amputee

Kaur

Kumar

Agarwal

2017

JBBBE

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The wavelet transform is an accurate, efficient and efficacious method to improve the quality of the myoelectric signal. Classification of the signal from upper limb using Surface Electromyogram (SEMG) signal has been the matter of extensive research. Number of methods and algorithms have been described by researchers to classify biomedical signals. The main aim of this paper to extract the different coefficient values from the given SEMG data by using Discrete Wavelet Transform (DWT). Afterward, random forest machine learning algorithm was used to identify the different shoulder movement of an upper limb amputee. The combination of wavelet coefficients and random forest exhibited the best performance with 99.2% accuracy for the classification of different shoulder motions. It was found that the different motion can be identified accurately and provide the fundamental information to develop an efficient prosthetic device.

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An experimental investigation of the impact of aggregation on the performance of data mining with logistic regression

Cited by 18 publications

References 32 publications

Predicting Next Trading Day Closing Price of Qatar Exchange Index Using Technical Indicators and Artificial Neural Networks

Predicting Next Trading Day Closing Price of Qatar Exchange Index Using Technical Indicators and Artificial Neural Networks

Comparing Data Mining Models in Academic Analytics

Wavelet Based Machine Learning Technique to Classify the Different Shoulder Movement of Upper Limb Amputee

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