Space-Time Tradeoffs in the Inverse B-W Transform
DCC '01 Proceedings of the Data Compression Conference
Towards parameter-free data mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Statistical Disk Cluster Classification for File Carving
IAS '07 Proceedings of the Third International Symposium on Information Assurance and Security
Information Theory and Statistical Learning
Information Theory and Statistical Learning
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
The Normalized Compression Distance Is Resistant to Noise
IEEE Transactions on Information Theory
| Hi-index | 0.00 |
We have applied the generalised and universal distance measure NCD-Normalised Compression Distance-to the problem of determining the type of file fragments. To enable later comparison of the results, the algorithm was applied to fragments of a publicly available corpus of files. The NCD algorithm in conjunction with the k-nearest-neighbour (k ranging from one to ten) as the classification algorithm was applied to a random selection of circa 3000 512-byte file fragments from 28 different file types. This procedure was then repeated ten times. While the overall accuracy of the n-valued classification only improved the prior probability from approximately 3.5% to circa 32-36%, the classifier reached accuracies of circa 70% for the most successful file types. A prototype of a file fragment classifier was then developed and evaluated on new set of data (from the same corpus). Some circa 3000 fragments were selected at random and the experiment repeated five times. This prototype classifier remained successful at classifying individual file types with accuracies ranging from only slightly lower than 70% for the best class, down to similar accuracies as in the prior experiment.