Design an Optimal Decision Tree based Algorithm to Improve Model Prediction Performance

Performance of decision trees is assessed by prediction accuracy for unobserved occurrences. In order to generate optimised decision trees with high classification accuracy and smaller decision trees, this study will pre-process the data. In this study, some decision tree components are addressed and enhanced. The algorithms should produce precise and ideal decision trees in order to increase prediction performance. Additionally, it hopes to create a decision tree algorithm with a tiny global footprint and excellent forecast accuracy. The typical decision tree-based technique was created for classification purposes and is used with various kinds of uncertain information. Prior to preparing the dataset for classification, the uncertain dataset was first processed through missing data treatment and other uncertainty handling procedures to produce the balanced dataset. Three different real-time datasets, including the Titanic dataset, the PIMA Indian Diabetes dataset, and datasets relating to heart disease, have been used to test the proposed algorithm. The suggested algorithm's performance has been assessed in terms of the precision, recall, f-measure, and accuracy metrics. The outcomes of suggested decision tree and the standard decision tree have been contrasted. On all three datasets, it was found that the decision tree with Gini impurity optimization performed remarkably well

Utilizing Analytical Hierarchy Process for Pauper House Programme in Malaysia

In Malaysia, the selection and evaluation of candidates for Pauper House Programme (PHP) are done manually. In this paper, a technique based on Analytical Hierarchy Technique (AHP) is designed and developed in order to make an evaluation and selection of PHP application. The aim is to ensure the selection process is more precise, accurate and can avoid any biasness issue. This technique is studied and designed based on the Pauper assessment technique from one of district offices in Malaysia. A hierarchical indexes are designed based on the criteria that been used in the official form of PHP application. A number of 23 samples of data which had been endorsed by Exco of State in Malaysia are used to test this technique. Furthermore the comparison of those two methods are given in this paper. All the calculations of this technique are done in a software namely Expert Choice version 11.5. By comparing the manual and AHP shows that there are three (3) samples that are not qualified. The developed technique also satisfies in term of ease of accuracy and preciseness but need a further study due to some limitation as explained in the recommendation of this paper

Missing attribute value prediction based on artificial neural network and rough set theory

In this research, artificial neural network (ANN) combined with rough set theory (RST), named as ANNRST, was proposed to predict missing values of attribute. The prediction of missing values of attribute was applied on heart disease data from UCI datasets. The ANN used was the multilayer perceptron (MLP) with resilient backpropagation learning. RST could reduce the dimensionality of attributes through its reduct. Reduct was used as input of ANN combined with decision attribute. By simulating missing values, the prediction accuracy of ANN was compared to ANNRST. The accuracy of ANNRST was also compared with missing data imputation of k-Nearest Neighbour (k-NN), most common attribute value method and ANN with piece-wise linear network-orthonormal least square feature selection (PLN-OLS). Simulation results showed that ANNRST could predict missing values with maximum accuracy close to ANN without dimensionality reduction (pure ANN) and outperformed k-NN, most common attribute value method, and ANN with PLN-OLS