What to Avoid When Solving Multi-Label Classification Problems - insideBIGDATA

What to Avoid When Solving Multi-Label Classification Problems – insideBIGDATA

Artificial intelligence is poised to be the next big thing in workplace efficiency. These models can read, interpret and find solutions to many business problems. One of the latest trends is multi-label classification, where AI can assign multiple labels to an entry. For example, it could tag a photo by every animal it can detect instead of finding a single item and focusing on that. Such a capability can further reduce the already small number of errors that algorithms can make.

However, this method has its challenges. If you’re working with a model with a multi-label classification problem, chances are you’ve come across something that needs to be fixed. Here are some common issues you may encounter and what to avoid when troubleshooting them.

1. Data cleaning

You will always need to clean your data before feeding it to the model. Entering too many irrelevant or inconsistent variables will only confuse the AI ​​and cause it to produce incorrect conclusions. Therefore, you need to follow a consistent and accurate data cleaning process to ensure that your algorithm remains efficient and – perhaps most importantly – correct.

However, you may run into problems while cleaning. You might accidentally delete information you thought was irrelevant or introduce a typo that confuses the AI. Each of these issues diminishes the validity of the dataset, creating errors that can lead to costly business decisions.

Troubleshooting Data Cleansing Errors

The easiest way to avoid and resolve any issues the team introduces during data cleaning is to follow your cleaning process to the letter. Take your time when inspecting and profiling to really assess what information is unnecessary or redundant. You can also use it to check for misspellings that might confuse the algorithm.

Also, don’t rush the verification step. You or someone else might have accidentally deleted an essential entry, failed to delete irrelevant data, or added white space where you didn’t need it. Consider this part of the process the most critical to preventing or resolving errors.

2. Label uncertainty

As you can imagine, many labels can apply to a single set of data. The new information may have similar attributes, but the AI ​​thinks it warrants another set of labels. However, you know that they must belong to the same classification.

The algorithm could analyze a set of applicants, making observing the talent pool much faster and easier. He sees one person who is a “great communicator” and another who promotes their “fast response times”, creating different labels for each. Having too many classifications defeats the purpose of AI and complicates your job.

Avoid label uncertainty issues

This problem means that the model becomes far too specific. Because it’s a machine, it takes the literal route more often than the implicit route. The previous example showed two instances of people saying the same thing that the model misinterpreted as being different. To reduce the chances of this issue, you will need to train the AI ​​more.

He must understand the correlations between the meaning of certain words. This may require further learning about the unconditional and conditional dependence of labels, which may help him recognize when words or labels essentially mean the same thing. Teaching the algorithm in this way will help reduce the number of classifications it creates, allowing it to remain as efficient as possible. In this process, avoid letting the AI ​​become too general while ensuring its specificity – reliance on labels can contribute to this.

3. Data imbalance

Data imbalance can be a widespread problem with multi-label classification. When the model focuses on higher instances of a label, it will not learn to interpret other inputs. This will negatively train your model and make your results less accurate.

For example, let’s say a bank is trying to find cases of fraud. The algorithm analyzes the information and concludes that 98% of the transactions were genuine and 2% fraudulent. The highest number is the majority class and the lowest is the minority. Having such a large majority can create a bias within the AI, making it less likely – in this banking example – to detect real cases of fraud.

Troubleshoot data imbalance issues

This problem will also require some retraining. You can start by practicing on the true distribution, but you may also need to consider the process of downsampling and upward weighting.

For a simpler example, consider a set of one fraud case for every 200 purchases. You can downsample this majority class by 20, so the balance goes from 1 fraud to 10 genuine transactions. Then, increase it by 20, which makes the majority class more important to the model. This process allows the AI ​​to see the minority class more frequently while responding to the urgency of the majority. Avoid incorrect balancing by using the correct ratio of undersampling to overweighting.

Make Multi-Label Classification Work Smoothly

Artificial intelligence for multi-label classification helps streamline many aspects of the workplace, from recruiting to marketing. However, you may need to adjust the model along the way. Keep an eye out for these typical issues to avoid common troubleshooting pitfalls.

About the Author

April Miller is senior IT and cybersecurity writer for ReHack Magazine. She specializes in AI, Big Data, and Machine Learning while writing on technology-related topics. You can find his work on ReHack.com and by following the ReHack Twitter page.

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