commit 4896d121688e89b80d4c3477f241d354fee2bec8
parent 93ddce2556c939120dac85c969f316d4da6757b8
Author: Alex Balgavy <alex@balgavy.eu>
Date: Thu, 3 Jun 2021 11:59:56 +0200
ML4QS
Diffstat:
2 files changed, 49 insertions(+), 0 deletions(-)
diff --git a/content/ml4qs/_index.md b/content/ml4qs/_index.md
@@ -5,3 +5,4 @@ title = 'Machine Learning for the Quantified Self'
# Machine Learning for the Quantified Self
1. [Introduction & Basics of Sensory Data](introduction-basics-of-sensory-data)
2. [Handling sensory noise](handling-sensory-noise)
+3. [Feature engineering](feature-engineering)
diff --git a/content/ml4qs/feature-engineering.md b/content/ml4qs/feature-engineering.md
@@ -0,0 +1,48 @@
++++
+title = 'Feature engineering'
++++
+
+# Feature engineering
+Creating useful features in different domains.
+
+## Time domain
+### Numerical
+- want to summarize values of numerical attribute i in a time window
+- assume temporal ordering $x_{1}^{i}, \dots, x_{N}^{i}$
+- select window size λ
+- for each time point t, select proper values $[x_{t-\lambda}^{i}, \dots, x_{t}^{i}]$
+- compute new value of feature, per time point, over each of those values
+
+### Categorical
+- generate patterns combining categorical values over time
+- consider a window size λ
+- consider different relationships: succession "(b)", co-occurrence "(c)"
+- support: what fraction of all time points does the pattern occur
+
+### Mixed
+Make categories from numerical values & apply categorical approach:
+- ranges (low, normal, high)
+- temporal relations (increasing, decreasing)
+
+
+### Pattern generation
+- only focus on patterns with sufficient support
+- start with patterns of single attribute value pairs with sufficient support
+
+## Frequency domain
+Consider series of values within a certain window of size λ.
+Perform Fourier transformation to see what frequencies we see in the window -- create sinusoid functions with different periods, with a base frequency (lowest frequency with complete sinusoid period).
+
+Get feature values: highest amplitude frequency, normalize.
+
+## Unstructured data - text
+Perform number of steps:
+- tokenization: identify sentences and words
+- lower case everything
+- stemming: identify stem of each word, map all variations of word to the stem
+- stop word removal: get rid of words like 'the' that are not predictive
+
+Approaches:
+- bag of words: count occurrences of n-grams (n consecutive words)
+- TF-IDF: frequency of words giving more weight to unique words
+- topic modeling: assume the text has some topics associated with words, look at topics instead of words
