Support vector machines (SVMs) are powerful algorithms widely used in machine learning and data analysis. At first glance, SVMs may seem like complex black boxes filled with mathematical jargon that only experts could understand. However, with a little bit of storytelling and analogy, we can unravel the beauty and logic of SVMs and make it more intuitive and accessible to everyone.
Let us start with a simple example. Imagine you are a farmer who wants to classify the type of crop growing in your field. You have data on the shape, color, and texture of the leaves, as well as the size and taste of the fruit. You noticed that there are two main types of crops that you want to distinguish: apples and oranges.
How would you go about classifying them? One option is to use a simple rule such as, “if the fruit is small and orange, it’s an orange. If the fruit is large and red, it’s an apple.” While this might work decently for relatively large crops, it becomes challenging and potentially unreliable when you have a lot of variables, subtle differences, and overlapping regions.
This is where SVMs come in handy. SVMs are a supervised machine learning algorithm designed to find the optimal hyperplane that separates two classes of data points with the maximum margin. In other words, it helps you draw a straight line, or a plane in 3D or higher dimensions, that best divides your data into two groups.
Going back to our farming example, imagine that we collected data on the size and color of the fruit, as well as the width and length of the leaves, for many apples and oranges. We could then plot this data in a two-dimensional space, with the size of the fruit on the x-axis and the color on the y-axis. Each data point represents a single fruit, either an apple or an orange, and our goal is to find the line that separates the two.
But how do we find the “best” line? In SVMs, the “best” line is the one that maximizes the margin between the two classes of data points. Let’s visualize this by drawing a plane that separates our data points in three dimensions. The plane is represented as a line in two dimensions.
The margin is the distance between the plane and the closest data point from each class. The intuition behind maximizing the margin is that it would increase the robustness and generalization of your classification model. It means that you’re unlikely to make mistakes when new data points come in because you’ve given the SVMs the most significant space allowed between two different classifications of data.
Finding the optimal hyperplane in high-dimensional spaces can be computationally expensive, as it requires solving a quadratic programming problem that involves linear and nonlinear kernels. However, there are different algorithms and techniques to speed up the process and improve the accuracy.
Let’s take another example of SVMs trying to differentiate between two different COVID-19 biological samples by featuring an SVM in a more sophisticated application scenario. Many researchers are leveraging machine learning techniques to help identify and locate COVID-19 sources in wastewater samples to help track the spread of COVID-19 outbreaks. One such approach involves using SVMs to classify COVID-19 samples by analyzing how light interacts with the samples.
Researchers can coax an organism in wastewater samples to polymerize using a special chemical procedure. Then, they can discern the molecular nature of how the organism looks by exposing the wastewater sample to different wavelengths of light. The light burns into the samples creating a signature, which machine learning algorithms can use to predict the molecule present in the sample.
The researchers want to find a suitable classification algorithm to distinguish between SARS-CoV-2 and non-SARS-CoV-2 (background) signatures. The process works by comparing the absorbance-based signals of two separate samples: one sample containing only the polymeride produced from the sample, and one sample containing only a reference library of species-specific polymerides. Researchers then created a principal component analysis (PCA) visual that can show a two-dimensional plane with seemingly random datapoints, which are mostly indistinguishable from one another. Yet, after running a supervised machine learning SVM algorithm, SARS-CoV-2 appeared as an outlier in the same two-dimensional space.
SVMs have a wide range of applications, from image recognition and natural language processing to financial predictions and cancer diagnosis. SVMs can be used to detect suspicious behavior in financial transactions, such as credit card fraud, by comparing patterns and anomalies in big data. SVMs are also used in speech recognition systems, where a computer can recognize words, phrases, and intonations by analyzing audio signals.
In conclusion, SVMs are powerful algorithms that are widely used in machine learning and data analysis. SVMs work by finding the optimal hyperplane that separates two classes of data points with the maximum margin. SVMs can be used in a variety of applications, from classifying crops to detecting fraudulent financial transactions. SVMs have proven to be a reliable and efficient tool in today’s data-driven world.