ml: basics

Machine learning

These pages will include tools, experiences, tutorials on how to do simple ML on our corpora

## Scope openVirus will almost certainly be limited to working with words (i.e. not images, speech, etc.).

There is no magic.

ML requires good data, good conceptual models, good annotation and constant testing and re-modelling. It may well require new code. The amount that can be done in a month is limited. None the less we can make useful discoveries in technology and hopefully some initial categorization.

Classification

Most of the operations will be classification. Wikipedia explains it well (https://en.wikipedia.org/wiki/Statistical_classification).

In statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. Examples are assigning a given email to the "spam" or "non-spam" class, and assigning a diagnosis to a given patient based on observed characteristics of the patient (sex, blood pressure, presence or absence of certain symptoms, etc.). Classification is an example of pattern recognition.

In the terminology of machine learning,[1] classification is considered an instance of supervised learning, i.e., learning where a training set of correctly identified observations is available. The corresponding unsupervised procedure is known as clustering, and involves grouping data into categories based on some measure of inherent similarity or distance.

Often, the individual observations are analyzed into a set of quantifiable properties, known variously as explanatory variables or features. These properties may variously be categorical (e.g. "A", "B", "AB" or "O", for blood type), ordinal (e.g. "large", "medium" or "small"), integer-valued (e.g. the number of occurrences of a particular word in an email) or real-valued (e.g. a measurement of blood pressure). Other classifiers work by comparing observations to previous observations by means of a similarity or distance function.

An algorithm that implements classification, especially in a concrete implementation, is known as a classifier. The term "classifier" sometimes also refers to the mathematical function, implemented by a classification algorithm, that maps input data to a category.

Features

From Wikipedia:

Feature vectors

Most algorithms describe an individual instance whose category is to be predicted using a feature vector of individual, measurable properties of the instance. Each property is termed a feature, also known in statistics as an explanatory variable (or independent variable, although features may or may not be statistically independent). Features may variously be binary (e.g. "on" or "off"); categorical (e.g. "A", "B", "AB" or "O", for blood type); ordinal (e.g. "large", "medium" or "small"); integer-valued (e.g. the number of occurrences of a particular word in an email); or real-valued (e.g. a measurement of blood pressure). If the instance is an image, the feature values might correspond to the pixels of an image; if the instance is a piece of text, the feature values might be occurrence frequencies of different words. Some algorithms work only in terms of discrete data and require that real-valued or integer-valued data be discretized into groups (e.g. less than 5, between 5 and 10, or greater than 10).

PMR: My experience is that it pays to have good feature extraction before the ML. That's why dictionaries and sectioning are so important. For example if you do word frequency analysis on our current corpora the commonest words in section titles are likely to be "Introduction" , "methods", etc. This would be useful to distinguish our corpus from sports reports ("Teams", "Fixtures") but for science they occur in most papers so there is a lot of "noise". However it might help to distinguish articles from reviews.