03 Data Preprocessing

Preprocessing Techniques¶

You don’t have to apply all these; it depends. You have to first understand the dataset.

Feature selection and Dimensionality reduction are used for biomarkers analysis

Types of Sampling¶

Random Sampling¶

Problem¶

It may lead to misclassification, as not all classes are represented proportionally in the sample.

Stratified Sampling¶

Different types of objects/classes with different frequency are used in the sample.

Useful especially in imbalanced dataset, where all the classes have large variation in their counts.

Ensures all classes of the population are well-represented in the sample.

Steps¶

• Draw samples from each class
  • equal samples, or
  • proportional samples, using % of the total of all classes
  • Gives us imbalanced dataset
• Combine these samples into a larger sample

Progressive/Adaptive Sampling¶

Useful when not sure about good sample size

Computationally-expensive

Steps¶

flowchart LR
s["Start with small sample (100-1000)"] -->
a[Apply data mining algorithm] -->
e[Evaluate results] -->|Increase Sample Size| s

e -->|Best Result Obtained| st[/Stop/]

Data Augmentation¶

• Images
• Flip
• Rotation
• Adding noise
• Warping
• Mixup
• Convert labels
• \(x' = \lambda x_i + (1-\lambda) x_j; y' = \lambda x_i + (1-\lambda) x_j\)

• 

• Fit it to a distribution

Dimensionality Reduction Algorithms¶

Feature Selection¶

flowchart LR

Attributes -->
ss[Search Strategy] -->
sa[/Subset of Attributes/] -->
Evaluation -->
sc{Stopping Criterion Reached?} -->
|No| ss

sc -->
|Yes| sel[/Select Attributes/] -->
Something

Mutual Information¶

Mutual information (MI) between two random variables is a non-negative value, which measures the dependency between the variables. It is equal to zero if and only if two random variables are independent, and higher values mean higher dependency. The function relies on nonparametric methods based on entropy estimation from k-nearest neighbors distances.

Brute Force Approach¶

Consider a set with \(n\) attributes. Its power set contains \(2^n\) sets. Ignoring \(\phi\), we get \(2^{n-1}\) sets.

Steps

• Evaluate the performance of all possible combinations of subsets
• Choose the subset of attributes which gives the best results

Embedded Approach¶

The data mining algorithm itself performs the selection, without human intervention

Eg: A decision tree automatically chooses the best attributes at every level

Builds a model in the form of a tree

• Internal nodes = labelled with attributes
• Leaf nodes = class label

Filter Approach¶

Independent of data mining algorithm

flowchart LR
o[(Original<br /> Feature Set)] -->
|Select<br /> Subset| r[(Reduced<br /> Feature Set)] -->
|Mining<br /> Algorithm| Result

eg: Select attributes whose evaluation criteria(pairwise correlation/Chi², entory) is as high/low as possible

Wrapper Approach¶

Use the data mining algorithm (capable of ranking importance of attributes) as a black box to find best subset of attributes

flowchart LR
o[(Original<br /> Feature Set)] -->
s[Select<br /> Subset] -->
dm[Mining<br /> Algorithm] -->
r[(Reduced<br /> Feature Set)] & s

subgraph Black Box["Ran n times"]
    s
    dm
end

Feature Engineering¶

Feature extraction¶

Mapping data to new space¶

• Time series data \(\to\) frequency domain
• For eg, fourier transformation

Feature construction¶

• Construct interaction terms from existing features
• Eg
• Area = length * breadth
• Density = mass/volume

Discretization¶

1. Sort the data in ascending order

2. Generate

   • \(n-1\) split points

   • \(n\) bins \(\to\) inclusive intervals (specified by the analyst)

Then convert using binarization. But, why?

Types¶

Binarization¶

Attribute Transform¶

Naming¶

Target Transform¶

Not recommended, as you will face all the disadvantages of MSLE

Box-Cox/Bickel-Doksum Transform¶

Back Transform¶

Back-transformed Prediction Intervals have correct coverage, but point forecasts are medians

Hence, if we need the mean, we need to perform correction. (Didn’t really understand the correction.)

Linear Basis Function¶

• \(\mu\) = pivot
• \(\sigma^2\) = bandwidth
• Higher, smoother
• Lower, sharper

LDA¶

Linear Discriminant Analysis, using Fisher Linear Discriminant

Maximizes separation using multiple classes, by seeking a projection that best discriminates the data

It is also used a pre-processing step for ML application

Goals¶

• Find directions along which the classes are best-separated (ie, increase discriminatory information)
• Maximize inter-class distance
• Minimize intra-class distance
• It takes into consideration the scatter(variance) within-classes and between-classes

Steps¶

1. Find within-class Scatter/Covariance matrix

\(S_w = S_1 + S_2\)

• \(S_1 \to\) Covariance matrix for class 1
• \(S_2 \to\) Covariance matrix for class 2

1. Find between-class scatter matrix

1. Find Eigen Value

2. Find Eigen Vector

3. Generate LDA Projection Normalized Eigen Vector

4. Generate LDA score (projected value) in reduced dimensions

Eigen Value¶

• \(\lambda =\) Eigen Value(s)
• If we get multiple eigen values, we only take the highest eigen value
• It helps preserve more information. How??
• \(I =\) Identity Matrix

We are taking \(A=S_w^{-1} S_B\) because taking \(S_w^{-1}\) helps us maximize \(\frac{1}{x}, x \in S_w\)

• Hence \(x\) is minimized
• Thereby, within-class distance is minimized

Eigen Vector¶

• \(\lambda =\) Highest eigen value
• \(V =\) Eigen Vector

Normalized Eigen Vector¶

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