# A Two-Phase Approach for Semi-Supervised Feature Selection
## 7 Conclusions  
In this paper, a novel two-phase scheme of feature selection under semi-supervised datasets and their classification has been presented. It is observed many times that collecting labels or classes of a large number of patterns can be quite expensive and may require a lot of effort. If we can manage to collect few patterns with labels after processing, it will be worth using the knowledge of those labels in the patterns to find the labels of other patterns as well. For this purpose, Fuzzy C-means clustering has been applied to cluster data. For the part of the data where labels are known, the class of each cluster can be decided by the highest number of patterns belonging to a particular class (reiterate in the case of tie). For the data where labels are not known, that can be clustered. Now, from the two sets of clusters, using a correspondence between the centroids, a suitable mapping can be devised. The pattern belonging to unlabeled data but closely matched to the cluster of labeled data can also be labeled the same as the latter. For feature selection, Pearson's correlation coefficient can be used to find the maximum relevant and minimum redundant features. After an extensive experimental study and investigation of several datasets, it is noted that the proposed scheme presents good classification accuracy while classifying unlabeled data. Moreover, a very small number of features is also required to obtain the accuracy that will save time and space. There have been some other schemes with variations available in the literature, but the proposed method presents a simple approach and utilizes the knowledge and properties of clusters to classify patterns without labels. Keeping these facts together, it can be concluded that the proposed two-phase approach for semi-supervised datasets-based feature selection can be applied to many other applications in science, engineering, data mining, health care, and similar other applications. The proposed method, however, fails to predict correct classes in the case of many classes' datasets; when we more than four classes. Although most of the real-time datasets have two or three classes and the proposed scheme deals well with such cases, it is future scope for the researchers to extend the present method to multi-class datasets.  
Conceptualization, A.S. and M.P.; methodology, A.S., S.P. and M.P.; software, D.G. and A.G.; validation, M.S.M., I.R. and C.-T.L.; formal analysis, A.S., S.P., D.G., A.G. and M.P.; investigation, M.S.M., I.R. and C.-T.L.; resources, A.S. and M.P.; data curation, M.S.M., D.G. and A.G.; writing--original draft preparation, A.S. and M.P.; writing--review and editing, A.S., S.P., M.S.M., D.G., A.G., I.R., C.-T.L. and M.P.; visualization, A.S., S.P. and M.P.; supervision, C.-T.L. and M.P.; project administration, A.S. and M.P.; funding acquisition, A.S., C.-T.L. and M.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Australian Research Council (ARC) under Grant DP180100670 and Grant DP180100656, in part by the U.S. Army Research Laboratory under Agreement W911 NF-10-2-0022, and in part by the Taiwan Ministry of Science and Technology under Grant MOST 106-2218-E-009-027-MY3 and MOST 108-2221-E-009-120-MY2.  
The authors declare no conflict of interest.