Similarity Measurement among Sectors Using Extended Relief-F Algorithm for Disk Recovery

This paper presents an approach to the recovery of damaged disks that measures the similarity among sectors using the Instance-based Feature Filtering algorithm and classification. After a hard-disk is destroyed, maliciously or accidentally, that hard-disk can be simply repaired using the recovery programs. However, there are always some sectors that cannot connect with the original file after recovery; typically, attempts are made to connect with the original file manually, or those attempts prove unsuccessful, the effort is abandoned. Therefore, an automatic process for finding the original file related to unconnected sectors is required. Typical methods assess the similarity among sectors and recommend relevant candidate sectors. Thus we propose an algorithm and process that can automatically find relevant sectors with the Extended Relief-F algorithm and the classifiers. We reformulated the Relief-F algorithm to select features by updating the difference functions and computation of the weight of features, apply those features to sectors, classify unconnected sectors, and recommend relevant candidate sectors. In the experiments, we also tested Information Gain, Odds Ratio and Relief-F for feature selection and compared them with the Extended Relief-F algorithm; additionally, we used the KNN and SVM classifiers for classification and estimation of relevant sectors. In the experimental results, the Extended Relief-F algorithm, compared with the others, performed best for all of the datasets.