Discriminant analysis for classification

Data mining encompasses a variety of analytic techniques that can help us analyze, understand, and extract insight from large sets of data. The various data mining techniques generally fall in three potential categories:

Classification – Use a dataset of characteristics (variables) for an observation to determine in which discrete group/class the observation belongs. For example:

  • Loan applications: use data about an individual or business to determine whether the applicant is a “good risk” (approve loan) or a “bad risk” (refuse loan)
  • Project evaluation: sort possible capital investment projects into “priority”, “medium” and “unattractive” based on their characteristics

Prediction – Use data to predict the value (or a range of reasonable values) of a continuous numeric response variable. For example:

  • Predict sales volume of a product in a future time period
  • Predict annual heating/cooling expenses for a set of buildings

Segmentation – Separate a set of observations into some collection of groups that are “most alike” within a group and “different from” other groups. For example:

  • Identify groups of customers that have similar ordering patterns across seasons of the year
  • Identify groups of items that tend to be purchased together

This blogpost will focus on (linear) Discriminant Analysis (DA), one of the oldest techniques for solving classification problems. DA attempts to find a linear combination of features that separates two or more groups of observations. It is related to analysis of variance (ANOVA), the difference being that ANOVA attempts to predict a continuous dependent variable using one or more independent categorical variables, whereas DA attempts to predict a categorical dependent variable using one or more continuous independent variable. DA is very similar to logistic regression, with the fundamental difference that DA assumes that the independent variables are normally distributed within each group. DA is also similar to Principal Component Analysis (PCA), especially in their application. DA differs from PCA in that it tries to find a vector in the variable space that best discriminates among the different groups, by modelling the difference between the centroids of each group. PCA instead tries to find a subspace of the variable space, that has a basis vector that best captures the variability among the different observations. In other words, DA deals directly with discrimination between groups, whereas PCA identifies the principal components of the data in its entirety, without particularly focusing on the underlying group structure[1].

To demonstrate how DA works, I’ll use an example adapted from Ragsdale (2018)[2] where potential employees are given a pre-employment test measuring their mechanical and verbal aptitudes and are then classified into having a superior, average, or inferior performance (Fig. 1).

DA_1

Fig. 1 – Employee classification on the basis of mechanical and verbal aptitude scores

The centroids of the three groups indicate the average value of each independent variable (the two test scores) for that group. The aim of DA is to find a classification rule that maximizes the separation between the group means, while making as few “mistakes” as possible. There are multiple discrimination rules available; in this post I will be demonstrating the maximum likelihood rule, as achieved though the application of the Mahalanobis Distance. The idea is the following: an observation i from a multivariate normal distribution should be assigned to group G_j that minimizes the Mahalanobis distance between i and group centroid C_j.

DA_2

Fig. 2 – Independent variables have different variances

The reason to use Mahalanobis (as opposed to say, Euclidean) is twofold: if the independent variables have unequal variances or if they are correlated, a distance metric that does not account for that could potentially misallocate an observation to the wrong group. In Fig. 2, the independent variables are not correlated, but X2 appears to have much larger variance than X1, so the effects of small but important differences in X1 could be masked by large but unimportant differences in X2. The ellipses in the figure represent regions containing 99% of the values belonging to each group. By Euclidean distance, P1 would be assigned to group 2,

DA_3

Fig. 3 – Independent variables are correlated

however it is unlikely to be in group 2 because its location with respect to the X1 axis exceeds the typical values for group 2. If the two attributes where additionally correlated (Fig. 3), we shall also adjust for this correlation in our distance metric. The Mahalanobis distance therefore uses the covariance matrix for the independent variables in its calculation of the distance of an observation to group means.

It is given by:  D_{ij}=\sqrt{(X_i-\mu_j)^TS^{-1}(X_i-\mu_j)} where X_i is the vector of attributes for observation i, \mu_j is the vector of means for group j, and S is the covariance matrix for the variables. When using the Mahalanobis Distance, points that are equidistant from a group mean are on tilted eclipses:

DA_4

Fig. 4 – Using the Mahalanobis Distance, points equidistant from a group mean are on tilted eclipses

 

 

For further discussion on the two distance metrics and their application, there’s also this blogpost. Applying the formula to the dataset, we can calculate distances between each observation and each group, and use the distances to classify each observation to each of the groups:

DA_5

Table 1 – Distance of each observation to each group, using Mahalanobis distance. Classification is estimated using the minimum between the three distances. Values in red indicate misclassifications.

If we tally up the correct vs. incorrect (in red) classifications, our classification model is right 83.33% of the time. Assuming that this classification accuracy is sufficient, we can then apply this simple model to classify new employees based on their scores on the verbal and mechanical aptitude tests.

[1] Martinez, A. M., and A. C. Kak. 2001. “PCA versus LDA.” IEEE Transactions on Pattern Analysis and Machine Intelligence 23 (2): 228–33. https://doi.org/10.1109/34.908974.

[2] Ragsdale, Cliff T. Spreadsheet Modeling and Decision Analysis: a Practical Introduction to Business Analytics. Eighth edition. Boston, MA: Cengage Learning, 2018.

 

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s