GENERALIZATION VS. DISCRIMINATION IN LEARNING

Tim Kovacs

Department of Computer Science, University of Bristol

Bristol, UK

kovacs@cs.bris.ac.uk

 

Andy J. Wills

Psychology, University of Exeter

Exeter, UK

a.j.wills@ex.ac.uk

Synonyms

[None]

Definition

Generalization and discrimination of observations is fundamental to all learning. We provide an introduction to key issues in the context of Machine Learning (ML), with references to the psychology literature, but can only touch on the basics. A related article deals with this subject in the context of comparative psychology and biology.

 

In ML, learning is often conceived as the search for a hypothesis which is a good fit to existing observations, on the assumption that it will also fit future observations well (with “hypothesis” being a very inclusive term in ML, that does not pre-judge the nature of the representation employed). However, there are typically very many hypotheses which fit existing observations well. A learner must therefore select which hypothesis to adopt, and some are more general than others. We'll use the somewhat contrived example of teaching a naēve learner to discriminate bluebirds from other birds. The learner has no prior experience of birds (it might be a robot or an extraterrestrial) and we teach it by pointing out birds and labeling them as bluebirds or not bluebirds. Suppose we first point out a fairly dark bird at mid-day and indicate that it is a bluebird. The learner might adopt the rather general hypothesis that all birds are bluebirds, which we will call h1. This is of course an overgeneralization, as the learner has not placed enough restrictions on what qualifies as a bluebird. Overgeneralization is not uncommon in human development (e.g. applying the label “doggie” to all four-legged creatures; applying linguistic regularities to exception items, e.g. “I go-ed to the park”). Conversely, the learner might adopt the overly specific hypothesis that only fairly dark birds seen around mid-day are bluebirds (h2), which places too many restrictions on what qualifies as a bluebird. An overly specific hypothesis fits observations too closely and we say that it overfits the data. There is some evidence that one characteristic of disorders such as autism is under-generalization of this sort.[1] Finally, the learner might adopt any of a number of hypotheses of generality between these two extremes, for example, that all dark birds are bluebirds (h3). All three hypotheses are consistent with the single observed bird, and nothing more can be learned from this observation. If the learner selects a hypothesis, but new observations reveal it to be overly general or overly specific, it should revise its hypothesis. For example, upon being shown a white swan, the learner should no longer cling to h1, the overgeneral hypothesis that all birds are bluebirds. (Note that it cannot disprove h1 if it only ever sees bluebirds – so called “negative examples” are very useful!). Selecting a hypothesis is an inductive (as opposed to deductive) problem, and any factor, other than observations, which contributes to this selection is part of the learner’s inductive bias. For example, it might be wary of overfitting its observations and avoid very specific hypotheses, even if they are consistent. The receptors through which information about observed objects is obtained may form part of the inductive bias – both by making some information unavailable (e.g. tones over about 20kHz for humans) and by substantial effects on perceived similarity (e.g. difference between the trichromatic color vision of most humans, the dichromatic vision of most other mammals, and the tetrachromatic vision of some birds).

 

The term “generalization” is used in several related ways in reference to learning. First, making inferences about later observations based on past ones is referred to as generalizing. If the learner above is shown a new bird and asked whether it is a bluebird, it must generalize from its past observations to answer the question. Hence, except in the special case where the current observation is identical to a previous one, all classification involves generalization. We have already seen a second use of “generalization”: a hypothesis which assigns observations to the same category is said to generalize over them, whereas a hypothesis which assigns observations to different categories is said to discriminate between them. There is also a third use of the term. When a learner replaces its current hypothesis with a more general one, it is said to generalize the hypothesis. The process of learning, to the extent that it is a matter of improving accuracy or some other measure, is a matter of generalizing and specializing hypotheses. Consequently, a central concern for cognitive psychologists is to determine how a learner generalizes, and a central concern for ML researchers is how to engineer learners which generalize appropriately.

 

Theoretical Background

In the bluebird example, hypotheses involve two dimensions: time of day and shade. It can be helpful to visualize low-dimensional hypotheses as decision boundaries as figure 1 does for the bluebird hypotheses.

 

Description: fig1.png

 

Figure 1. On the left is h1: all birds are bluebirds. In the middle h2: only fairly dark birds seen around

mid-day are bluebirds. On the right h3: all dark birds are bluebirds. In each case the first observed

bird (a fairly dark bird seen at mid-day) is shown with a B (for Bluebird).

 

Note that, while h2 as drawn is quite specific, we could have made it as small as a single point, but that seemed less useful as both an illustration and as a basis for learning. Note also that h3 completely ignores the time of day. On one hand it might be irrelevant, since a given bird's species does not depend on the time of day, but on the other hand, we might be more likely to encounter certain species at certain times. In general, it is not trivial to determine which dimensions are the most useful for learning; in ML this is called feature selection. Humans and other animals seem to track the correlation between stimulus dimensions and outcomes (e.g. category labels) and, over time, adjust the amount of attention these dimensions receive.

 

Now suppose more birds are added as in figure 2 (left), with B for Bluebird and O for Other bird. If we restrict our hypotheses to be rectangular, the smaller rectangle is the most specific hypothesis consistent with the observations, while the larger rectangle is the most general consistent hypothesis. Any rectangle within them will also be consistent, and the selection of a particular consistent hypothesis by the learner will be due to its inductive bias. Note that we have very few observations, which can greatly increase the risk of overfitting and overgeneralizing. With more observations we might find that the larger and smaller hypotheses were much closer, and hence that there was less difference between the consistent hypotheses. Note also that, in reality, bluebirds and other birds would tend to overlap in our chosen 2-dimensional space. This would complicate matters significantly, so, for simplicity, let us overlook it for the moment.

 

Description: fig2.png

           

Figure 2. Left: the smallest and largest consistent rectangles. Middle: a hypothesis which fits the

observed bluebirds more closely than any rectangle. Right: a new, misidentified bird (M) reduces the

 largest consistent rectangle.

 

The restriction to rectangular hypotheses is a form of bias, as it heavily constrains the generalizations which can be made. Why might we use rectangles in ML? One reason is they are easy to define, both geometrically and logically. Geometrically, we need only specify one corner and two offsets from it. Logically, we need only give an interval for each dimension, e.g. “all birds with shades between S1 and S2 and seen between 2pm and 4pm are bluebirds”. We could, however, use a language which defines circles, or indeed arbitrary shapes.

 

Given that rectangles constrain the generalizations which can be made, why not use arbitrary shapes? The example in the middle of figure 2 illustrates that a complex shape can fit the observed bluebirds much more closely than any rectangle. However, as we noted earlier, we do not want to overfit our observations, as that typically results in poor predictive accuracy on future observations (that is, poor generalization). More complex hypotheses are better able to fit observations than simpler, less flexible, hypotheses, and are hence more likely to overfit them. Consequently, simpler, less flexible hypotheses are often preferable, which gives us a basis for Ockham's razor, a well-known bias which holds that we should prefer the simplest hypotheses which fit the observations to date. The Minimum Description Length principle is a formalization of Ockham's razor and an important concept in information theory and computational learning theory.

Important Scientific Research and Open Questions

Our bluebird example is simplified in a number of ways. Rectangular hypotheses are often too inflexible and both ML systems and psychological models use others. Humans and other animals do find it easier to learn hypotheses whose bounds are parallel to psychologically meaningful dimensions, which is an example of their inductive bias.

 

A very significant simplification, and bias, is that we ignored the possibility of noise, which makes overfitting much more of a problem. By noise we mean, for example, recording an incorrect time of day or shade, or mistaking a bluebird for a non-bluebird. This last is shown in figure 2 (right) where the M represents a new bluebird mistaken for a non-bluebird. This seriously reduces the most general consistent rectangle, as shown with dashed lines. Because noise is so common, practical learning methods cannot insist on fully consistent hypotheses.

 

Another simplification and bias was the assumption that bluebirds can be defined by a single rectangle; in general, concepts may require a set of such hypotheses. For example, “fairly dark birds seen quite early or quite late” requires two rectangles. Real-world categories are often considered to have a family “resemblance” structure, in that they contain many properties that are characteristic, rather than a set of properties that are singly necessary and jointly sufficient.

 

Our bluebird example also lacks any mechanism for capturing the graded nature of real-world categories. Chickens and penguins are both birds, but people rate chickens as more typical of the bird category than penguins, and can verify the accuracy of sentences such as “ a chicken is a bird” more rapidly than sentences such as “a penguin is a bird”. People’s classification of atypical items is also noisy, varying from day to day. For example, the statement “a tomato is a vegetable” may be considered correct on one occasion, but incorrect on the next. Hence, people’s categories can have fuzzy boundaries. Other simplifications (and biases) in the example are that concepts do not overlap and do not change over time.

 

We will not discuss methods (“algorithms”) by which machines learn, or psychological models of learning, but there are many. Humans appear to have access to multiple classification processes (e.g. rule extraction; generalization from specific known instances). Further, the relative dominance of those processes in determining people’s responses seems to be affected both by the nature of the objects to be classified, and the amount of experience people have of objects of that type.

 

In ML terms, our example is a case of supervised learning, since the observed birds have been labeled by a teacher. Of course, very often there is no teacher, in which case we have an unsupervised learning problem, where the learner must optimize some criterion of its own choice. For example, in clustering, the learner groups observations into clusters based on their similarity, but the definition of “similar” is left to the learner. Reinforcement learning is a third paradigm, in which the learner responds to rewards and punishments. There is also growing interest in newer paradigms, such as semi-supervised learning, which exploits both labeled and unlabeled data. ML is finding many new applications, of which many involve modeling human behavior, such as predicting customer preferences on websites, or blocking unwanted email. Generalization and discrimination are slightly different in each paradigm but nonetheless central to all.

Cross-References

Abstract concept learning in animals

Animal perceptual learning

Artificial learning and Machine Learning

Categorical learning

Classification learning

Concept formation

Concept learning

Concept learning of machines

Learning algorithms

 

 References

Flach, P. (forthcoming). Machine Learning. The Art and Science of Algorithms that Make Sense of Data. Cambridge University Press.

 

Mitchell, T. (1997). Machine Learning. McGraw Hill.

 

Pearce, J.M. (2008). Animal learning and cognition: An introduction. Psychology Press.

 

Pothos, E.M. and Wills, A.J. (Eds.) (2011). Formal approaches in categroization. Cambridge University Press.

Pearce

 

Russell, S. & Norvig, P. (2003). Artificial Intelligence. A Modern Approach. Second editon. Prentice Hall.

 

Wills, A.J. (2011). Models of categorization. In D. Reisberg (Ed.). Oxford handbook of cognitive psychology. Oxford University Press.

 

 

 

 

Appendix – Key Terms

 

 

Inductive reasoning [an article]

 

Minimum Description Length principle: an inductive bias used in machine learning which suggests that shorter (i.e. simpler) hypotheses should be preferred.

 

Ockham's razor: the heuristic that we should prefer the simplest hypotheses which fit the observations to date.

 

Overfitting: when a learner fits its training data too closely, and does not draw conclusions which are as general as they should be.

 

Overgeneralization: when a learner draws inappropriately general conclusions from its training data.

 

Reinforcement Learning [an article]

 

Semi-supervised Learning. Obtaining labeled data for supervised learning can be costly, but often large amounts of unlabeled data can be obtained cheaply. Semi-supervised learning exploits both at once and is useful when only limited labeled data is available.

 

Supervised Learning [an article]

 

Unsupervised Learning [an article]

 



[1] Note the bewildering alternatives for describing generality: an overgeneral hypothesis underfits observations, while an overspecific hypothesis overfits them. Furthermore, “overgeneral” is logically equivalent to “underspecific” and “overspecific” is logically equivalent to “undergeneral”. However, “overgeneral” and “overspecific” are preferred to the “underspecific” and “undergeneral”.