Comparing the Performance of Predictive Models Constructed Using the Techniques of Feed-Forword and Generalized Regression Neural Networks

Artificial Neural Network (ANNs) is an efficient machine learning method that can be used to fits model from data for prediction purposes. It is capable of modelling the class prediction as a nonlinear combination of the inputs. However, a number of factors may affect the accuracy of the model created using this approach. The choice of network type and how the network is optimally configured plays important role in the performance of a predictive model created using neural network techniques. This paper compares the accuracy of two typical neural network techniques used for creating a predictive model. The techniques are feed-forward neural network and the generalized regression networks. The model created using both techniques are evaluated for correctness. The resulting outputs show that, the Generalized Regression Neural Network (GRNN) consistently produces a more accurate result. Findings further show that, the fitting of the network predictive model using the technique of Feed-forward Neural Network (FNN) records error value of 1.086 higher than the generalized regression network

An enhanced feed-forward neural networks and a rule-based algorithm for predictive modelling of students' academic performance

Feed-forward Neural Networks, is a multilayer perceptron and a network structure capable of modelling the class prediction as a nonlinear combination of the inputs. The network has proven its suitability in solving several complex tasks. But sometimes, it has challenges of over-fitting, especially when fitting models from massive data of varied data points. This necessitates its enhancement in order to strengthen its performance. Such enhancement would ensure a predictive network model that can generalize well with a set of untrained data. In this research, in order to alleviate the possibility of over-fitting in a network predictive model, a dynamic partitioning of the dataset is proposed. Also, for a more efficient exploration of students‟ data collected for this research, a Rule-Based Algorithm is proposed and implemented. The predictive models emanated from the two approaches were evaluated in order to validate their effectiveness. The enhancement done to the Feed-forward Neural Networks (FNN) in the first approach, ensure partitioning of the dataset that is based on the size of the data available for creating the model. The evaluation carried out on the Enhanced Feed-forward Neural Network (EFNN) models show that, there is a decrease in error from 0.261 to 0.029. Similarly, another set of 2000 students‟ data is trained, the error recorded when the network model is simulated with untrained 500 data show that, there is a reduction in error from 0.0095 to 0.00033. Most of the training performance generated from the network models created also shows that, the EFNN has lower errors and converge faster. The implementation of the rule-based algorithm proposed in the second approach, shows outputs that are consistently accurate. Its efficiency is compared to some existing techniques reported in the literature for the predictive modelling of students‟ academic performance. Findings from the comparison show that, the proposed RBA explores students‟ data much better. It can also serve as an alternative algorithm to the use of machine learning techniques in the exploration of students‟ data for prediction purposes

Risk Status Prediction and Modelling Of Students’ Academic Achievement - A Fuzzy Logic Approach

Several students usually fall victims of low grade point at the end of their first year in the institution of higher learning and some were even withdrawn due to their unacceptable grade point average (GPA); this could be prevented if necessary measures were taken at the appropriate time. In this paper, a modelusing fuzzy logic approach to predict the risk status of students based on some predictive factors is proposed. Some basic information that has some correlations with students’ academic achievement and other predictive variables were modelled, the simulated model shows some degree of risk associated with their past academic achievement. The result of this study would enable the teacher to pay more attention to student’s weaknessesand could also help school management in decision making, especially for the purpose of giving scholarship to talented students whose risk of failure was found to be very low; while students identified as having high risk of failure, could be counselled and motivated with a view to improving their learning abilit

Datasets Size: Effect on Clustering Results

The recent advancement in the way we capture and store data pose a serious challenge for data analysis. This gives a wider acceptance to data mining, being an interdisciplinary field that implements algorithm on stored data with a view to discovering hidden knowledge. Most people that keep records, however, are yet to reap the benefits of this tool, this is due to the general notion that a large datasets is required to guarantee reliable results. However, this may not be applicable in all cases. In this paper, we proposed a research technique that implements descriptive algorithms on numeric datasets of varied sizes. We modeled each subset of our data using EM clustering algorithm; two different numbers of partitions (k) were estimated and used for each experiment. The clustering results were validated using external evaluation measure in order to determine their level of correctness. The approach unveils the implication of datasets size on the clusters formed and the impact of estimated number of partitions

Using an Enhanced Feed-Forward BP Network for Predictive Model Building From Students’ Data

Feed-forward, Back Propagation (BP) Network is a network structure capable of modeling the class prediction as a nonlinear combination of the inputs. The network has proven its suitability in solving several complex tasks, most especially when trained with appropriate algorithms. This study presents an enhancement of this network with a view to boosting its prediction accuracy. The paper proposed a modification of the data partitioning function in the regular feed-forward network. A predictive model is constructed based on the proposed partition, while the second model is based on the partition of the existing network. Both models are trained and simulated with sets of untrained data. The mean absolute error is computed for both models and their error values are compared. Comparison of their results shows that the enhanced network consistently delivers higher accuracy and generalized better than the existing network in its regular structure; as there was a decrease in error from 0.261 to 0.016. The enhanced network has also shown its suitability in the fittings of models from students’ data for prediction purposes

Using an Enhanced Feed-Forward Neural Network Technique for Prediction of Students' Performance

The newly admitted students for the undergraduate programmes in the institutions of higher learning sometimes experi ence some academic adjustment that is associated with stress; many factors have been attributed to this, which most times, results in the high percentage of fail ure and low Grade Point Average (GPA). Computing the earlier academjc achievements for these sets of students would make one to be abreast of their level of knowledge academically, in order to be well-infonned of their areas of weakness and strength. In this paper, an enhancement of Feed-forward Neural Network fo r the creation of a network model to predict the students' performance based on their historical data is proposed. In the course of experimentations with Matlab software, two network models are crea ted using the existing and enhanced feed-forward neural network techniques. The abiliry of these models to generalize is measured using simulation methods. The enhanced network model consistently shows a high degree of accuracy and predicts well. The performance of students predicted as outstanding, can also be supponed financially in the fom1 of scholarship; while those that are found to be academically weak can be encouraged and rightly counseled at the early stage of their studies

Cluster Analysis of Data Points using Partitioning and Probabilistic Model-based Algorithms

Exploring the dataset features through the application of clustering algorithms is a viable means by which the conceptual description of such data can be revealed for better understanding, grouping and decision making. Some clustering algorithms, especially those that are partitioned-based, clusters any data presented to them even if similar features do not present. This study explores the performance accuracies of partitioning-based algorithms and probabilistic model-based algorithm. Experiments were conducted using k-means, k-medoids and EM-algorithm. The study implements each algorithm using RapidMiner Software and the results generated was validated for correctness in accordance to the concept of external criteria method. The clusters formed revealed the capability and drawbacks of each algorithm on the data points

Evaluating the Effect of Dataset Size on Predictive Model Using Supervised Learning Technique

Learning models used for prediction purposes are mostly developed without paying much cognizance to the size of datasetsthat can produce models of high accuracy and better generalization. Although, the general believe is that, large dataset is needed to construct a predictive learning model. To describe adata setas large in size, perhaps, iscircumstance dependent, thus, what constitutesa dataset to be considered as being big or small is vague.In this paper, the ability of predictive model to generalize with respect to a particular size of data when simulated with new untrained input is examined. The study experiments on three different sizes of data using Matlab programto create predictive models with a view to establishing if the sizeof data has any effect on the accuracy of a model.The simulated output of each model is measured using theMean Absolute Error (MAE) and comparisons are made. Findings from this study reveals that, the quantity of data partitioned for the purpose of training must be of good representation of the entire sets and sufficient enough to span through the input space. The results of simulating the three network models also shows that, the learning model with the largest size of training setsappearsto be the most accurate and consistently delivers a much better and stable results