Random multinomial logit

Random multinomial logit

In statistics and machine learning, random multinomial logit (RMNL) is a technique for (multi-class) statistical classification using repeated multinomial logit analyses via Leo Breiman's random forests.

Rationale for the new method

Several learning algorithms have been proposed to handle multiclass classification. While some algorithms are extensions or combinations of intrinsically binary classification methods ("e.g.", multiclass classifiers as one-versus-one or one-versus-all binary classifiers), other algorithms like multinomial logit (MNL) are specifically designed to map features to a multiclass output vector. MNL’s stability has a proven track record in many disciplines, including transportation research and CRM (customer relationship management). Unfortunately, MNL cannot overcome the curse of dimensionality, thereby implicitly necessitating feature selection, "i.e.", the selection of a best subset of variables of the input feature set. In contrast to binary logit, to date, software packages mostly lack any feature selection algorithm for MNL. This absence constitutes a serious problem for several application areas.

Recently, random forests, ("i.e.", a classifier combining a forest of decision trees grown on random input vectors and splitting nodes on a random subset of features) have been introduced for the classification of binary and multiclass outputs. Feature selection is implicitly incorporated during each tree construction. RMNL, a random forest of multinomial logit models, attempts to overcome the feature selection difficulty of MNL.

Application

The developers of the RMNL technique (Prinzie & Van den Poel, 2008) show in their application paper the usefulness of the technique for cross-sell analysis in customer relationship management.

References

* [http://dx.doi.org/10.1016/j.eswa.2007.01.029 Prinzie, A., Van den Poel, D. (2008). Random Forests for multiclass classification: Random MultiNomial Logit, Expert Systems with Applications, 34(3), 1721–1732.] Generalization of Random Forests to choice models like the Multinomial Logit Model (MNL): Random Multinomial Logit.

See also

*Random naive bayes
*Random forest


Wikimedia Foundation. 2010.

Игры ⚽ Нужен реферат?

Look at other dictionaries:

  • Multinomial logit — In statistics, economics, and genetics, a multinomial logit (MNL) model, also known as multinomial logistic regression, is a regression model which generalizes logistic regression by allowing more than two discrete outcomes. That is, it is a… …   Wikipedia

  • Random naive Bayes — extends the Naive Bayes classifier by adopting the random forest principles: random input selection (bagging, i.e. bootstrap aggregating) and random feature selection ( [Breiman, 2001] ). Naive Bayes classifier Naive Bayes is a probabilistic… …   Wikipedia

  • Random forest — In machine learning, a random forest is a classifier that consists of many decision trees and outputs the class that is the mode of the classes output by individual trees. The algorithm for inducing a random forest was developed by Leo Breiman… …   Wikipedia

  • Multinomial distribution — Multinomial parameters: n > 0 number of trials (integer) event probabilities (Σpi = 1) support: pmf …   Wikipedia

  • Negative multinomial distribution — notation: parameters: k0 ∈ N0 the number of failures before the experiment is stopped, p ∈ Rm m vector of “success” probabilities, p0 = 1 − (p1+…+pm) the probability of a “failure”. support …   Wikipedia

  • Predictive analytics — encompasses a variety of techniques from statistics and data mining that analyze current and historical data to make predictions about future events. Such predictions rarely take the form of absolute statements, and are more likely to be… …   Wikipedia

  • Régression logistique multinomiale aléatoire — En statistique et en apprentissage automatique, Logit multinomial aléatoire (« random multinomial logit (RMNL) ») est une technique de classification automatique multi classe utilisant des analyses logistiques multinomiales répétées… …   Wikipédia en Français

  • List of mathematics articles (R) — NOTOC R R. A. Fisher Lectureship Rabdology Rabin automaton Rabin signature algorithm Rabinovich Fabrikant equations Rabinowitsch trick Racah polynomials Racah W coefficient Racetrack (game) Racks and quandles Radar chart Rademacher complexity… …   Wikipedia

  • Feature selection — Feature selection, also known as variable selection, feature reduction, attribute selection or variable subset selection, is the technique, commonly used in machine learning, of selecting a subset of relevant features for building robust learning …   Wikipedia

  • Discrete choice — In economics, discrete choice problems involve choices between two or more discrete alternatives, such as entering or not entering the labor market, or choosing between modes of transport. Such choices contrast with standard consumption models in …   Wikipedia

Share the article and excerpts

Direct link
Do a right-click on the link above
and select “Copy Link”