Sign up for our newsletter and get the latest big data news and analysis.

What to Avoid When Solving Multilabel Classification Problems

Artificial intelligence is quickly becoming the next big thing in workplace efficiency. These models can read, interpret and find solutions to many companies’ problems. One of the latest trends is multilabel classification, where the AI can assign multiple labels to an input. For example, it could label a photo by every animal it can detect instead of finding a single element and focusing on that. Such an ability can further reduce the already slim number of errors the algorithms can make.

However, this method has its challenges. If you are working with a model with a multilabel classification problem, there is a likely chance you will run into something in need of fixing. Here are a few common issues you may encounter and what to avoid when solving them.

1. Data Cleaning

You’ll always need to cleanse your data before feeding it to the model. Inputting too many irrelevant or inconsistent variables will only confuse the AI and cause it to produce incorrect conclusions. Therefore, you must follow a consistent and precise data-cleaning process to ensure your algorithm stays efficient and — perhaps most importantly — correct.

However, you may run into issues while cleaning. You might accidentally remove information you thought was irrelevant or introduce a typo that throws off the AI. Each of these issues decreases the validity of the data set, creating fallacies that can lead to costly business decisions.

Resolving Data Cleaning Mistakes

The simplest way to avoid and resolve any problems the team introduces during data cleaning is to follow your cleansing process to the letter. Take your time during inspection and profiling to truly gauge what information is unnecessary or redundant. You can also use this to double-check for spelling errors that could introduce confusion within the algorithm.

Additionally, do not rush the verification step. You or someone else could have accidentally deleted an essential input, failed to remove irrelevant data or added white space where you didn’t need to. Consider this part of the process as the most critical to prevent or solve any errors.

2. Label Uncertainty

As you can imagine, many labels can apply to a single data set. New information may have similar attributes, but the AI believes it warrants another set of labels. However, you know they should belong to the same classification.

The algorithm could analyze a set of job applications, making observing the talent pool much faster and more straightforward. It sees one person who is an “excellent communicator” and another who promotes their “speedy response times,” creating different labels for each. Having too many classifications defeats the purpose of the AI and recomplicates your job.

Avoiding Label Uncertainty Problems

This issue means the model is getting far too specific. Because it is a machine, it takes the literal route more often than the implied one. The previous example showed two instances of people saying the same thing that the model misinterpreted as different. To lower the chances of this problem, you will need to train the AI further.

It needs to understand the correlations between what certain words mean. It may require deeper learning on unconditional and conditional label dependence, which can help it recognize when words or labels mean essentially the same thing. Teaching the algorithm this way will help narrow down the number of classifications it creates, allowing it to stay as efficient as possible. In this process, avoid letting the AI get too general while also ensuring its specificity — label dependence can help with that.

3. Data Imbalance

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

For instance, say a bank is trying to find cases of fraud. The algorithm does a run-through of the information and concludes 98% of the transactions were genuine and 2% were fraudulent. The larger number is the majority class and the lower one is the minority. Having such a large majority can create a bias within the AI, making it less likely — in this bank example — to detect actual instances of fraud.

Solving Issues With Data Imbalance

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

For a more straightforward example, consider a set of one instance of fraud for every 200 purchases. You could downsample that majority class by 20, so the balance becomes one fraud to 10 genuine transactions. Next, upweight it by 20, which gives the majority class greater importance to the model. This process allows the AI to see the minority class more frequently while also addressing the urgency of the majority. Avoid improper balancing by using the proper ratio of downsampling to upweighting.

Make Multilabel Classification Run Smoothly

Artificial intelligence for multilabel classification helps streamline many aspects of the workplace, from recruitment to marketing. However, you may need to adjust the model along the way. Keep an eye out for these typical problems to avoid the common pitfalls of solving them.

About the Author

April Miller is a senior IT and cybersecurity writer for ReHack Magazine who specializes in AI, big data, and machine learning while writing on topics across the technology realm. You can find her work on ReHack.com and by following ReHack’s Twitter page.

Sign up for the free insideBIGDATA newsletter.

Join us on Twitter: https://twitter.com/InsideBigData1

Join us on LinkedIn: https://www.linkedin.com/company/insidebigdata/

Join us on Facebook: https://www.facebook.com/insideBIGDATANOW

Leave a Comment

*