# A Two-Phase Approach for Semi-Supervised Feature Selection
## 5 Experiments  
The proposed two-phase method was run on an i5 machine using MATLAB. The semi-supervised dataset was considered to contain two types of patterns: One part that contains the labels and the other part that does not contain labels. For finding the reduced number of features in the whole dataset, we used the approach proposed by [PERSON] et al. (2016) and used the Karl Pearson Correlation coefficient method with MRmr. For obtaining a feature subset with MRmr, Algorithm 1 is applied. Form all combinations of features from the feature set as mentioned in Algorithm 1. Find out the CA with every combination of the reduced set of features formed as above by the \(1\)-NN method. Find out the feature subset that provides maximum CA. Thus, we have a reduced set of features; transform the whole dataset with the reduced number of features. Divide the reduced dataset into two parts: One part that contains class labels and the other part where the class labels are kept hidden. This completes Phase-I of the proposed approach. Prior to beginning Phase-II, apply any clustering algorithm (K-means clustering in present work) to divide the reduced dataset obtained in the first part into the number of clusters that is the same as the number of available classes used in the first part. Now, collect the centroids (equal to the number of classes in the first part). If we have \(c\) classes and \(r\) reduced number of features, we obtain \(c\) cluster centroids (rows) and each centroid has \(r\) values (columns). In each cluster, the class attached to the majority of patterns will be considered as the class of that cluster. This is verified by several experiments used in this method. Phase-II starts with the second part of the reduced dataset, which is unsupervised; no classes are known. Divide this dataset into a number of clusters that is the same as the number of available classes of the dataset. Find out the centroids of the obtained clusters. Each cluster will contain a number of patterns with unknown class. Compare these centroids to those obtained in the first part of Phase-1. Find out a good mapping of each centroid in the second part with that obtained in the first part. Form the pairs of clusters such that each pair contains the first cluster centroid from the first part and the second cluster centroid from the second part of the dataset. As the class of the first cluster of this pair is known, as mentioned in Phase-I, the same class will be assigned to the second cluster belonging to the second part. The second part is already clustered in the beginning and the class of each cluster of the second part is also decided using this approach. Therefore, each pattern belonging to the clusters of the second part of the dataset will be the same as the class tagged with the individual clusters decided before. For verification, we check the class of each pattern of clusters of the second part with the original dataset. The accuracy of the matching of patterns is computed. In this manner, we also find the class of the second part, which was unknown originally. **Methods Used:** For classification, we used 1-NN in Phase-I. The Karl Pearson Coefficient was used to find relevance and redundancy. Fuzzy C-means clustering [46] was used to divide datasets into \(C\) clusters. \(C\) was also used as the number of available classes. The summaries of these methods are presented in Section 3. We used nine datasets summarized in Table 1. The datasets are divided into two parts. The ratios of known (labeled) classes against unknown classes (