3 Ways to Apply AI to Small Data Sets

14.02.22
~5min
4

It is better to use AI algorithms on small data sets for results free of human errors and false results when applied correctly. Here are some methods to apply AI to small data sets.

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Artificial intelligence and data science work together for better data collection, categorization, analysis, and interpretation. However, we only ever hear of using AI to understand big data sets. This is because small data sets are usually easily understood by people, and applying AI to analyze and interpret them isn't necessary.
These days, many businesses and manufacturers integrate AI into the production line, slowly creating data scarcity. And unlike big companies, many setups cannot collect massive training sets due to risk, time, and budget limitations. This results in the neglect or incorrect application of AI solutions to small data sets.
As most companies don't know how to benefit from AI application on small data sets correctly, they blindly apply it to make future predictions based on previous files. Unfortunately, this results in wrong and risky decisions.
So, it is essential to learn the correct ways to apply AI to small data sets and avoid any misinterpretation.

The 5 Correct Ways of AI Application on Small Data Sets

It is better to use AI algorithms on small data sets for results free of human errors and false results when applied correctly. You also save time and resources usually spent on manually interpreting small data.
Here are some methods to apply AI to small data sets:

1. Few-Shot Learning

The few-shot learning model introduces a small amount of training data to AI as a reference for new dataset interpretation. It is a commonly used approach in computer vision because it doesn't require many examples for identification.
For example, financial analysis systems do not require an extensive inventory to be effective. So, instead of overloading the AI system with a lot of information, you input a profit and loss statement template according to the system's capacity.
Unlike other AI systems, if you enter more information into this template, it will result in false results.
When you upload sample data in the AI system, it learns the pattern from the training data set for future small data set interpretation. The appealing point of the few-shot learning model is that you don't need an extensive training data set to train the AI, making it operational at low cost and effort.

2. Knowledge Graphs

The knowledge graphs model creates secondary data sets by filtering through a big original data set. It is used for storing interlinked descriptions and characteristics of events, objects, real situations, and theoretical or abstract concepts.
In addition to working as data storage, this model simultaneously encodes semantics underlying the specific data set.
The primary function of the knowledge graphs model is the organization and structuring of important points from the data set to integrate information collected from various sources. A knowledge graph is labeled to associate specific meanings. There are two main components in a graph - nodes and edges. Nodes are two or more items, and edges represent the connection and relation between them.
You can use knowledge graphs to store information, integrate data, and manipulate data through multiple algorithms to highlight new information. Moreover, they are handy for organizing small data sets to make them highly explainable and reusable.

3. Transfer Learning

Companies avoid applying AI on small data sets because they are uncertain about the results. The same methods that produce effective results for big data sets backfire and create false results. However, the transfer learning method makes similar and reliable results despite the size of the data set.
Transfer learning uses one AI model as a starting point but obtains the results with a new AI model. In short, it is the process of transferring knowledge from one model to another.
This model is primarily used in the computer vision field and naturally processed languages. The reason is that these tasks require a lot of data and computing power. So, using transfer learning cuts down the extra time and effort.
The new data set must be similar to the original training data set to apply the transfer learning model on small data. During application, remove the end of the neural network and add a fully connected layer similar to new data set classes. After this, randomize the weights of fully connected layers while freezing the previous network's weights. Now, update and train the AI network according to the new fully connected and operational layer.
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