You want to use a database of information about tissue samples to classify future tissue samples as either normal or mutated. You are evaluating an unsupervised anomaly detection method for classifying the tissue samples. Which two characteristic support this method? (Choose two.)
A. There are very few occurrences of mutations relative to normal samples.
B. There are roughly equal occurrences of both normal and mutated samples in the database.
C. You expect future mutations to have different features from the mutated samples in the database.
D. You expect future mutations to have similar features to the mutated samples in the database.
E. You already have labels for which samples are mutated and which are normal in the database.
|
Download Printable PDF. VALID exam to help you PASS. |
 |
A, D
Anomaly detection involves identifying rare data instances (anomalies) that come from a different class or distribution than the majority (which are simply called “normal†instances). Given a training set of only normal data, the semi-supervised anomaly detection task is to identify anomalies in the future. Good solutions to this task have applications in fraud and intrusion detection. The unsupervised anomaly detection task is different: Given unlabeled, mostly-normal data, identify the anomalies among them.
https://www.science.gov/topicpages/u/unsupervised+anomaly+detection
its A and D
Anomaly detection has two basic assumptions:
*Anomalies only occur very rarely in the data.
*Their features differ from the normal instances significantly.
A believe is wrong as The main idea of unsupervised anomaly detection algorithms is to detect data instances in a dataset, which deviate from the norm.
So B& C should be right.
A is correct, no B.
A because “Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal”, B is for Supervised anomaly detection https://en.wikipedia.org/wiki/Anomaly_detection
I would say b and d . future samples have to be as close to the ones of the sample as possible.