science

Incremental decision tree

Incremental decision tree is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Incremental decision tree rather than just read about it. In short: An incremental decision tree algorithm is an online machine learning algorithm that outputs a decision tree. Many decision tree methods, such as C4.5, construct a tree using a complete dataset.

Key takeaways

Incremental decision tree belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Incremental decision tree to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Incremental decision tree from memory before moving on to harder problems.

Reference excerpt

An incremental decision tree algorithm is an online machine learning algorithm that outputs a decision tree. Many decision tree methods, such as C4.5, construct a tree using a complete dataset. Incremental decision tree methods allow an existing tree to be updated using only new individual data instances, without having to re-process past instances. This may be useful in situations where the entire dataset is not available when the tree is updated (i.e. the data was not stored), the original data set is too large to process or the characteristics of the data change over time.

Applications Online learning Data streams Concept drift Data which can be modeled well using a hierarchical model. Systems where a user-interpretable output is desired.

Methods Here is a short list of incremental decision tree methods, organized by their (usually non-incremental) parent algorithms.

CART family CART (1984) is a nonincremental decision tree inducer for both classification and regression problems. developed in the mathematics and statistics communities. CART traces its roots to AID (1963)

incremental CART (1989) Crawford modified CART to incorporate data incrementally.

ID3/C4.5 family ID3 (1986) and C4.5 (1993) were developed by Quinlan and have roots in Hunt's Concept Learning System (CLS, 1966) The ID3 family of tree inducers was developed in the engineering and computer science communities.

ID3' (1986) was suggested by Schlimmer and Fisher. It was a brute-force method to make ID3 incremental; after each new data instance is acquired, an entirely new tree is induced using ID3. ID4 (1986) could incorporate data incrementally. However, certain concepts were unlearnable, because ID4 discards subtrees when a new test is chosen for a node. ID5 (1988) didn't discard subtrees, but also did not guarantee that it would produce the same tree as ID3. ID5R (1989) output the same tree as ID3 for a dataset regardless of the incremental training order. This was accomplished by recursively updating the tree's subnodes. It did not handle numeric variables, multiclass classification tasks, or missing values. ID6MDL (2007) an extended version of the ID3 or ID5R algorithms. ITI (1997) is an efficient method for incrementally inducing decision trees. The same tree is produced for a dataset regardless of the data's presentation order, or whether the tree is induced incrementally or non incrementally (batch mode). It can accommodate numeric variables, multiclass tasks, and missing values. Code is available on the web. [1] note: ID6NB (2009) is not incremental.

Other Incremental Learning Systems There were several incremental concept learning systems that did not build decision trees, but which predated and influenced the development of the earliest incremental decision tree learners, notably ID4. Notable among these was Schlimmer and Granger's STAGGER (1986), which learned disjunctive concepts incrementally. STAGGER was developed to examine concepts that changed over time (concept drift). Prior to STAGGER, Michalski and Larson (1978) investigated an incremental variant of AQ (Michalski, 1973), a supervised system for learning concepts in disjunctive normal form (DNF). Experience with these earlier systems and others, to include incremental tree-structured unsupervised learning, contributed to a conceptual framework for evaluating incremental decision tree learners specifically, and incremental concept learning generally, along four dimensions that reflect the inherent tradeoffs between learning cost and quality: (1) cost of knowledge base update, (2) the number of observations that are required to converge on a knowledge base with given characteristics, (3) the total effort (as a function of the first two dimensions) that a system exerts, and the (4) quality (often consistency) of the final knowledge base. Some of the historical context in which incremental decision tree learners emerged is given in Fisher and Schlimmer (1988), and which also expands on the four factor framework that was used to evaluate and design incremental learning systems.

VFDT Algorithm Very Fast Decision Trees learner reduces training time for large incremental data sets by subsampling the incoming data stream.

VFDT (2000) CVFDT (2001) can adapt to concept drift, by using a sliding window on incoming data. Old data outside the window is forgotten. VFDTc (2006) extends VFDT for continuous data, concept drift, and application of Naive Bayes classifiers in the leaves. VFML (2003) is a toolkit and available on the web. [2]. It was developed by the creators of VFDT and CVFDT.

OLIN and IFN OLIN (2002) IOLIN (2008) — based on Info-Fuzzy Network (IFN)

GAENARI gaenari

See also Concept drift Decision tree Machine Learning Online learning

References

External links ITI code. http://www-lrn.cs.umass.edu/iti/index.html VFML code. http://www.cs.washington.edu/dm/vfml/ C++ incremental decision tree. https://github.com/greenfish77/gaenari

Worked examples

Example 1 — a first encounter with Incremental decision tree

Start with the simplest possible case. Write down what Incremental decision tree claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Incremental decision tree before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Incremental decision tree ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Incremental decision tree

In research: Incremental decision tree appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Incremental decision tree in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Incremental decision tree is common in secondary-school and first-year university syllabi. It links to neighbouring topics Decision trees, so understanding it makes those chapters shorter.
In everyday life: Look for Incremental decision tree outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Incremental decision tree in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Incremental decision tree means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Incremental decision tree out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Incremental decision tree in simple terms?

An incremental decision tree algorithm is an online machine learning algorithm that outputs a decision tree. Many decision tree methods, such as C4.5, construct a tree using a complete dataset.

Why does Incremental decision tree matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Incremental decision tree?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Incremental decision tree.