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Subsections

1.1 Machine Learning

1.1 Machine Learning

Machine learning is concerned with machines which improve with experience and reason inductively or abductively in order to: optimise, approximate, summarise, generalise from specific examples to general rules, classify, make predictions, find associations, propose explanations, and propose ways of grouping things. For simplicity we will restrict ourselves to classification and optimisation problems.

Inductive Generalisation

Inductive generalisation refers to the inference of unknown values from known values. Induction differs from deduction in that the unknown values are in fact unknowable, which gives rise to fundamental limitations in what can be learned. (If at a later time new data makes all such values known the problem ceases to be inductive.) Given the unknown values are unknowable, we assume they are correlated with the known values and we seek to learn the correlations. We formulate our objective as maximising a function of the unknown values. In evolutionary computation this objective is called the fitness function, whereas in other areas the analogous feedback signal may be known as the error function, or by other names. There is no need for induction if: i) all values are known and ii) there is enough time to process them. We consider two inductive problems: function optimisation and learning. We will not deal with abduction.

1-Max: a Typical Optimisation Problem

The 1-max problem is to maximise the number of 1s in a binary string of length . The optimal solution is trivial for humans although it is less so for EAs. The representation of this problem follows. Input: none. Output: bit strings of length . Data generation: we can generate as many output strings as time allows, up to the point where we have enumerated the search space (in which case the problem ceases to be inductive). Training: the fitness of a string is the number of 1s it contains. We can evaluate a learning method on this task by determining how close it gets to the known optimal solution. In more realistic problems the optimum is not known and we may not even know the maximum possible fitness. Nonetheless, for both toy and realistic problems we can evaluate how much training was needed to reach a certain fitness and how a learning method compares to others.

Classification of Mushrooms: a Typical Learning Problem

Suppose we want to classify mushroom species as poisonous or edible given some training data consisting of features of each species (colour, size and so on) including edibility. Our task is to learn a hypothesis which will classify new species whose edibility is unknown. Representation: the input is a set of nominal attributes and the output is a binary label indicating edibility. Data generation: a fixed dataset of input/output examples derived from a book. Typically the dataset is far, far smaller than the set of possible inputs, and we partition it into train and test sets. Training: induce a hypothesis which maximises classification accuracy on the train set. Evaluation: evaluate the accuracy of the induced hypothesis on the test set, which we take as an indication of how well a newly encountered species might be classified.

Terminology in Supervised Learning

Although many others exist, we focus on the primary machine learning paradigm: standard Supervised Learning (SL), of which the preceding mushroom classification task is a good example. In SL we have a dataset of labelled input/output pairs. Inputs are typically called instances, examples or exemplars and are factored into attributes (also called features) while outputs are called classes (for classification tasks) or the output is called the dependent variable (for regression tasks).

Comparison of Supervised Learning and Optimisation

In SL we typically have limited training data and it is crucial to find a good inductive bias for later use on new data. Consequently, we must evaluate the generalisation of the induced hypothesis from the train set to the previously unused test set. In contrast, in optimisation we can typically can generate as much data as time allows and we can typically evaluate any output. We are concerned with finding the optimum output in minimum time, and, specifically, inducing which output to evaluate next. As a result no test set is needed.

Issues in Supervised Learning

A great many issues arise in SL including overfitting, underfitting, producing human readable results, dealing with class imbalances in the training data, asymmetric cost functions, noisy and non-stationary data, online learning, stream mining, learning from particularly small datasets, learning when there are very many attributes, learning from positive instances only, incorporating bias and prior knowledge, handling structured data, and using additional unlabelled data for training. None of these will be dealt with here.

Next: Arguments For and Against Up: Introduction Previous: Introduction   Contents
T Kovacs 2011-03-12