Chapter 4 Flashcards Preview

lambda is the window size: expresses the amount of discrrete time steps considered

> lambda = 1: consider two instances, the current instance and instance before

1. succession - one before the other

2. co-occurence - occur at same time point

support: how often the pattern occurs in the data compared to the number of time points in our data

> for all instances, check whether the pattern occurs within the selected window size

1. define minimal support threshold theta

2. generate all possible patterns of size 1 that meet theta

3. iteratively extend possible patterns by 1 until desired size k

> this much more efficient than simply checking all possible combinations

> the support of a new k-pattern can never be greater than the support value of the least supported subpattern it includes

ignore the first lambda instances, as they do not have sufficient history available

derive categorical features from numerical features: two methods:

1. if certain ranges are known (low, normal, high)

2. if no such information is available, calculate slope

> if slope above certain threshold: increasing/decreasing

> else stable

base frequency:

f0 = 2*pi / lambda +1

> lambda + 1 is the number of data points we consider

> 2*pi is one full sinusoid period

> base frequency is the lowest frequency that can fit a whole period into our window

0 * f0

1 * f0

...

lambda * f0

lambda +1 * f0

>>> starts at zero...

frequency domain features:

1. amplitude: frequency with highest amplitude describes the most important frequency in the considered window

2. frequency weighted signal average: weighted average frequency within considered window

3. power spectral entropy: describes how much information is contained within the signal 

> whether there are one or a few discrete frequencies standing out of all others

1. tokenization

> identify sentences and words within sentences

2. lower case

> change uppercase to lowercase

3. stemming

> identify stem of each word to reduce words to their stem

> map all different variations to a single term

4. stop word removal

> remove known stop words as they are not likely to be predictive

bag of words:

1. define n-grams of words (unigrams, bigrams etc)

2. count number of occurences for each n-gram in text irrespective the order of appearance

3. value for new attribute is number of occurence for that text

> can be binary with only true (occuring in text) and false (not occuring in text)

1. do bag of words >>> term frequency in document = a

2. normalize: divide the total number of instances by the number of instances that contain the n-gram = idf

> the higher the number, the more unique the n-gram is

3. compute a*idf = tf_idf

> n-grams that are unique are weighted more

> this avoidy very frequent words to become dominant in our attributes

topic modeling: extract more high level topics from text

1. assume W words (generated by poisson) and a distribution over topics

2. for each word in W select a topic based on the probabilities

3. for each word assume that its topic is wrong but the other topics are correct

4. probabilistically assign word w to a topic based on

> what topics are in document

> number of times word w assigned to particular topic

5. repeat

>>> create on attribute per topic and assign a value based on the observed frequencies of words and weights assigned to the words for the topic

overlapping windows are of course highly correlated

> each window differs just in one point from adjacent instances

> this is likely to cause overfitting

solution: set a maximum overlap for windows and remove instances for which this criterion is not met

(typically 50% overlap is allowed)