Beginning Anomaly Detection Using



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Beginning Anomaly Detection Using Python-Based Deep Learning

invasive species in Figure 

2-8


.

The invasive species is larger, has a bigger circumference, and has a longer tailfin 

on average (compare Figure 

2-7


 to Figure 

2-8


). However, the problem is that while the 

average specimen of each species has some noticeable distinctions between them, there 

is plenty of overlap between the two species where some of the native species grow large, 

some of the mutant species are just smaller, both have varying tail fin sizes, etc. so the 

differences might not always be as clear-cut.

To find out the extent of this infiltration, a large group of fishermen have been 

assembled and presented with the task of identifying the species of each fish in a catch 

of 1,000 fish. In this case, assume that each fisherman will randomly profile each fish to 

determine whether it is a member of the native species or not.

Now onto the evaluations. Each fisherman first picks a random feature to judge 

the samples on: the length of the fish, the circumference of the fish, or the proportion 

of its tail fin to its overall length. Then, the fisherman picks a random value between 

the known minimum and maximum values of the corresponding measurement for the 

native species and splits all the fish accordingly (all fish with the relevant measurement 

equal to or bigger than the picked value go right, and everything else goes left, for 

example). The fisherman repeats the entire process over and over again until every 

single fish has been partitioned and a “tree” of fish has been created.

Figure 2-8.  This is an example of the new, mutant species that has been released 

into the lake

Chapter 2   traditional Methods of anoMaly deteCtion




36

In this case, each individual fisherman represents a tree in the isolation forest, and 

the resulting trees of the entire group of fishermen represent an isolation forest. Now, 

given a random fish in the entire catch, you can get an anomaly score to see how many 

of the fisherman found that this fish is anomalous. Based on the threshold you pick for 

the anomaly score, you can label certain fish as the invasive species and the others as the 

native species.

However, the problem is that this is not a perfect system; there will be some invasive 

fish that pass off as native fish, and some native fish that pass off as invasive species. 

These cases represent false positives and false negatives.




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