## 3 Demonstration of Filter-Wrapper Approach in Evaluating Chronic Disease Datasets
\begin{table}
\begin{tabular}{c c} \hline \hline
**Computational Techniques** & **A Brief Description of the Methods** \\ \hline Ranker Algorithm & It generates a ranked attribute set thereby specifying the number of attributes chosen among the ranked attribute list. \\ Best First Search Algorithm (BFS) & It uses a priority queue and a heuristic to find the most promising attribute and later analyse that attribute. It determines the importance of prediction nature of an attribute and the redundancy level between a pair of attributes. Attribute sets with high class correlation and low inter-correlation are suitable. \\ Chi-square Method & It demonstrates the significance of an attribute by calculating the value of the chi-squared metric in relation to the class label. \\ Information Gain Method (IG) & It computes the value of an attribute by determining the net gain in information in association with the class. \\ InfoCain (Class, Attribute) = H (Class)-H (Class|Attribute). \\ It evaluates the impact of attribute selection by taking a limited attribute set. This limited attribute set is selected either by initial ordering or a ranking process. \\ The direction of search can be forward or floating \\ forward with selective backward steps. \\ It performs a greedy forward or backward search \\ through an attribute subset space. It starts with all or no attributes and terminates when further addition or removal of attributes reduces the evaluation. It may also generate a ranked attribute set through traversal of attribute subsets and reordering the selected attributes. It evaluates attribute sets by using a learning approach and later a Cross validation technique to compute the classification accuracy of the learning method for a certain set of attributes. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Computational Methods used in the study. \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithms Used** & **Library Package Used in WEKA** \\ \hline Ranker Algorithm & weka.attributeSelection.Ranker \\ Best First Search Algorithm & weka.attributeSelection.BestFirst \\ CFS Algorithm & weka.attributeSelection.CfsSubsetEval \\ Chi-square Method & weka.attributeSelection.ChiSquaredAttributeEval \\ Information Gain Method & weka.attributeSelection.InfoCainAttributeEval \\ Decision Tree Classifier & weka.classifiers.trees.J48 \\ Greedy Step wise Algorithm & weka.attributeSelection.GreedyStepwise \\ WrapperSubsetEval Method & weka.attributeSelection.WrapperSubsetEval \\ Linear Forward Selection Method & weka.attributeSelection.LinearForwardSelection \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Algorithms and their packages in WEKA tool. In this study the procedure is partitioned into two different stages. The first part is the attribute evaluator through which attribute subsets are evaluated. The second part includes the search scheme that enables the attributes to search the space of possible subsets and find the most relevant attribute subset. The attribute evaluator section is further partitioned into two different steps: the filter approach method and the wrapper approach. On the one hand, in the filter approach, attribute selection does not depend on classification algorithms. The attributes are selected on the basis of their ranking in statistical tests. This study includes three popular filter methods, namely, CFS, IG and Chi-square methods. These filter methods are used to select relevant attributes from pre-processed chronic disease datasets. Then, the attributes are ranked by the Ranker algorithm in WEKA. The Ranker method is responsible for generating a ranked attribute list for attribute evaluators. The output of this method is the reduced and optimized attribute set which is used for classification using the Decision Tree algorithm. The second part of the attribute evaluator is the wrapper approach where a wrapper method in combination with a suitable search algorithm generates an optimal set of attributes.