About the impact of grouping classes in multiclass classification [D]
Our take
The question posed by /u/neonhexe on Reddit – whether grouping infrequent classes in multiclass classification is detrimental – strikes at a core challenge in machine learning: effectively handling imbalanced datasets. It’s a question many practitioners grapple with, particularly when dealing with real-world scenarios where some categories are inherently less represented than others. Their intuition, that forcing a model to learn convoluted decision boundaries to accommodate disparate classes within a single “Other” category, is generally sound. The suggestion to frame this as an out-of-distribution (OOD) detection problem offers a more elegant solution, aligning with the broader trend toward robust models that can identify and handle unseen data. This resonates with efforts to build more reliable AI systems, as demonstrated by Waymo’s focus on scalability and robust performance in its next-gen robotaxi, Waymo’s cheaper, next-gen robotaxi is now open to all riders in these three cities. Both approaches highlight the importance of adapting models to the realities of imperfect data.
The core issue isn’t simply about the *number* of samples, but also the *diversity* within those samples. Grouping wildly different breeds of dogs into an "Other" category, as the user illustrates, creates a significant challenge for the model. It's effectively being asked to generalize from dissimilar examples, potentially leading to decreased accuracy for the more common breeds as the model struggles to disentangle the "Other" category's complexity. The proposed OOD detection approach, on the other hand, allows the model to focus on learning the characteristics of the well-represented classes while relegating the unusual examples to a separate, manageable category. This mirrors the innovative work being done in resource-constrained AI, where models are being designed to operate with incredibly limited memory, such as the diffusion model trained on just 264KB of RAM [Trained an diffusion model that runs on 264KB of RAM [P]]( /post/trained-an-diffusion-model-that-runs-on-264kb-of-ram-p-cmt01kz470j1xmi9zsrt6tdu0). The principle is the same: efficient resource allocation and intelligent handling of data limitations.
However, the transition to an OOD detection framework isn't always straightforward. It requires careful calibration and a clear understanding of what constitutes “out-of-distribution.” A poorly calibrated OOD detector might flag legitimate, albeit rare, examples as anomalous, leading to a loss of valuable information. Furthermore, simply discarding infrequent classes entirely might be a missed opportunity. Techniques like data augmentation, synthetic data generation, or even transfer learning from related datasets can help bolster the representation of those classes without resorting to a catch-all approach. The discussion around AI’s potential in healthcare also underscores the need for nuanced approaches to data challenges, as highlighted in the article about a startup’s view on curing cancer AI isn’t close to curing cancer. This startup says it knows what it will take, where data scarcity and quality are paramount.
Ultimately, the optimal strategy for handling infrequent classes in multiclass classification is context-dependent. There's no one-size-fits-all solution. The conversation initiated by /u/neonhexe underscores a crucial point: data imbalance isn't just a technical problem; it's a design challenge that requires careful consideration of the model's objectives, the nature of the data, and the potential consequences of different approaches. As we move towards increasingly complex AI systems, the ability to gracefully handle data scarcity and diversity will be paramount to ensuring their reliability and generalizability. A key question to watch is whether we'll see a shift towards more adaptive classification systems that dynamically adjust their behavior based on the distribution of incoming data, rather than relying on static, pre-defined categories.
A premise: I hope this question is "worth" of this subreddit, I did a decent amount of research before posting, I thought it was potentially interesting enough for it, but possibly not basic enough for r/learnmachinelearning .
Is there any agreement/indication about how harmful (if at all) it is, in the context of multiclass classification, to group together multiple classes for which you may have for instance too few samples?
A practical example: imagine you're training a dog breed classifier, based on images. You have a lot of examples for the most common breeds, but then you may have a long tail of less common breeds for which maybe you have a handful of examples each, not enough to get a meaningful training set, so you decide to group all classes for which you have less than `N` samples in the same category "Other breed". In this catch-all category you may have dogs that might look quite different from each other, like idk chihuahuas and huge wolf-like dogs (I'm not a dog person, don't know breed names).
My intuition (which may very well be wrong) is that doing so would force the model to learn some weirdly-shaped hyperplanes to separate points that live kind of far away from each other in the latent space (because of the thing that dogs in that category may look quite different from each other), as opposed to splitting the space in more "regular" parts.
Maybe in this case it would make more sense to treat the "other dogs" issue as trying to detect out of distribution samples instead? In that case should one only keep the samples for the classes that are enough represented in the dataset and throw away the rest (or at least don't create the catch-all category for training).
Thanks in advance for any useful pointer :)
[link] [comments]
Read on the original site
Open the publisher's page for the full experience