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Learning classifier system

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2D visualization of LCS rules learning to approximate a 3D function. Each blue ellipse represents an individual rule covering part of the solution space. (Adapted from images taken from XCSF[1] with permission from Martin Butz)

Learning classifier systems, or LCS, are a paradigm of rule-based machine learning methods that combine a discovery component (e.g. typically a genetic algorithm) with a learning component (performing either supervised learning, reinforcement learning, or unsupervised learning).[2] Learning classifier systems seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions (e.g. behavior modeling,[3] classification,[4][5] data mining,[5][6][7] regression,[8] function approximation,[9] or game strategy). This approach allows complex solution spaces to be broken up into smaller, simpler parts.

The founding concepts behind learning classifier systems came from attempts to model complex adaptive systems, using rule-based agents to form an artificial cognitive system (i.e. artificial intelligence).

Methodology

The architecture and components of a given learning classifier system can be quite variable. It is useful to think of an LCS as a machine comprised of several interacting components. Components may be added or removed, or existing components modified/exchanged to suit the demands of a given problem domain (like algorithmic building blocks) or to make the algorithm flexible enough to function in many different problem domains. As a result, the LCS paradigm can be flexibly applied to many problem domains that call for machine learning. The major divisions among LCS implementations are as follows: (1) Michigan-style architecture vs. Pittsburgh-style architecture, (2) reinforcement learning vs. supervised learning, (3) incremental learning vs. batch learning, (4) online learning vs. offline learning, (5) strength-based fitness vs. accuracy-based fitness, and (6) complete action mapping vs best action mapping. These divisions are not necessarily mutually exclusive. For example, XCS[10], the best known and best studied LCS algorithm, is Michigan-style, was designed for reinforcement learning but can also perform supervised learning, applies incremental learning that can be either online or offline, applies accuracy-based fitness, and seeks to generate a complete action mapping.

Elements of a Generic LCS Algorithm

Keeping in mind that LCS is a paradigm for genetic-based machine learning rather than a specific method, the following outlines key elements of a generic, modern (i.e. post-XCS) LCS algorithm.

Environment

The environment is the source of data upon which an LCS learns. It can be an offline, finite training dataset (characteristic of a data mining, classification, or regression problem), or an online sequential stream of live training instances. Each training instance is assumed to include some number of features (also referred to as attributes, or independent variables), and a single endpoint of interest (also referred to as the class, action, phenotype, prediction, or dependent variable).

Rule/Classifier

A rule is a context dependent relationship between state values and some prediction. Rules typically take the form of an {IF:THEN} expression, (e.g.  {IF ‘condition’ THEN ‘action’}, or as a more specific example, {IF ‘red’ AND ‘octagon’ THEN ‘stop-sign’}).   An individual rule is not in itself a model, since the rule is only applicable when it’s condition is satisfied.Rules Many different rule representations have been proposed

of Increasing interest in supervised learning applications has arguably of LCS in these domains. Keeping in mind that

Therefore rule-based machine learning methods typically identify a set of rules that collectively comprise the prediction model, or the knowledge base

can perform both online learning and offline learning forms complete action maps,

Rules/Classifiers

Matching

s a paradigm for genetics-based machine learning, t

Historically, LCS has been synonymous with reinforcement learning. Keeping this in mind, it is more accessible to first describe a generic LCS algorithm from the perspective of supervised learning. The following description is most closely based on a supervised learning adaptation of the XCS algorithm called UCS

History

Early Years

John Henry Holland was best known for his work popularizing genetic algorithms (GA), through his ground-breaking book "Adaptation in Natural and Artificial Systems"[11] in 1975 and his formalization of Holland's schema theorem. In 1976, Dr. Holland conceptualized an extension of the GA concept to what he called a "cognitive system",[12] and provided the first detailed description of what would be come known as the first learning classifier system in the paper "Cognitive Systems based on Adaptive Algorithms".[13] This first system, named Cognitive System One (CS-1) was conceived as a modeling tool, designed to model a real system (i.e. environment) with unknown underlying dynamics using a population of human readable rules. The goal was for a set of rules to perform online machine learning to adapt to the environment based on infrequent payoff/reward (i.e. reinforcement learning) and apply these rules to generate a behavior that matched the real system. This early, ambitious implementation was later regarded as overly complex, yielding inconsistent results.[2][14]

Beginning in 1980, Kenneth De Jong and his student Stephen Smith took a different approach to rule-based machine learning with (LS-1), where learning was viewed as an offline optimization process rather than an online adaptation process.[15][16][17] This new approach was more similar to a standard genetic algorithm but evolved independent sets of rules. Since that time LCS methods inspired by the online learning framework introduced by Holland at the University of Michigan have been referred to as Michigan-style LCS, and those inspired by Smith and De Jong at the University of Pittsburgh have been referred to as Pittsburgh-style LCS.[2][14] In 1986, Holland developed what would be considered the standard Michigan-style LCS for the next decade.[18]

Other important concepts that emerged in the early days of LCS research included (1) the formalization of a bucket brigade algorithm (BBA) for credit assignment/learning,[19] (2) selection of parent rules from a common 'environmental niche' (i.e. the match set [M]) rather than from the whole population [P],[20] (3) covering, first introduced as a create operator,[21] (4) the formalization of an action set [A],[21] (5) a simplified algorithm architecture,[21] (6) strength-based fitness,[18] (7) consideration of single-step, or supervised learning problems[22] and the introduction of the correct set [C],[23] (8) accuracy-based fitness[24] (9) the combination of fuzzy logic with LCS[25] (which later spawned a lineage of fuzzy LCS algorithms), (10) encouraging long action chains and default hierarchies for improving performance on multi-step problems,[26][27][28] (11) examining latent learning (which later inspired a new branch of anticipatory classifier systems (ACS)[29]), and (12) the introduction of the first Q-learning-like credit assignment technique.[30] While not all of these concepts are applied in modern LCS algorithms, each were landmarks in the development of the LCS paradigm.

The Revolution

Interest in learning classifier systems was reinvigorated in the mid 1990's largely due to two events; the development of the Q-Learning algorithm[31] for reinforcement learning, and the introduction of significantly simplified Michigan-style LCS architectures by Stewart Wilson.[32][10] Wilson's Zeroth-level Classifier System (ZCS)[32] focused on increasing algorithmic understandability based on Hollands standard LCS implementation.[18] This was done, in part, by removing rule-bidding and the internal message list, essential to the original BBA credit assignment, and replacing it with a hybrid BBA/Q-Learning strategy. ZCS demonstrated that a much simpler LCS architecture could perform as well as the original, more complex implementations. However, ZCS still suffered from performance drawbacks including the proliferation of over-general classifiers.

In 1995, Wilson published his landmark paper, '"Classifier fitness based on accuracy" wherein he introduced an eXtended Classifier System (XCS). XCS took the simplified architecture of ZCS and added an accuracy-based fitness, a niche GA (acting in the action set [A]), an explicit generalization mechanism called subsumption, and an adaptation of the Q-Learning credit assignment. XCS was popularized by it's ability to reach optimal performance while evolving accurate and maximally general classifiers as well as it's impressive problem flexibility (able to perform both reinforcement learning and supervised learning) . XCS later became the best known and most studied LCS algorithm and defined a new family of accuracy-based LCS. ZCS alternatively became synonymous with strength-based LCS. XCS is also important, because it successfully bridged the gap between LCS and the field of reinforcement learning. Following the success of XCS, LCS were later described as reinforcement learning systems endowed with a generalization capability.[33] Reinforcement learning typically seeks to learn a value function that maps out a complete representation of the state/action space. Similarly, the design of XCS drives it to form an all-inclusive and accurate representation of the problem space (i.e. a complete map) rather than focusing on high payoff niches in the environment (as was the case with strength-based LCS). Conceptually, complete maps don't only capture what you should do, or what is correct, but also what you shouldn't do, or what's incorrect. Differently, most strength-based LCSs, or exclusively supervised learning LCSs seek a rule set of efficient generalizations in the form of a best action map (or a partial map). Comparisons between strength vs. accuracy-based fitness and complete vs. best action maps have since been examined in greater detail.[34][35]

In the Wake of XCS

XCS inspired the development of a whole new generation of LCS algorithms and applications. In 1995, Congdon was the first to apply LCS to real-world epidemiological investigations of disease [36] followed closely by Holmes who developed the BOOLE++,[37] EpiCS,[38] and later EpiXCS[39] for epidemiological classification. These early works inspired later interest in applying LCS algorithms to complex and large-scale data mining tasks epitomized by bioinformatics applications. In 1998, Stolzmann introduced anticipatory classifier systems (ACS) which included rules in the form of 'condition-action-effect, rather than the classic 'condition-action' representation.[29] ACS was designed to predict the perceptual consequences of an action in all possible situations in an environment. In other words, the system evolves a model that specifies not only what to do in a given situation, but also provides information of what will happen after a specific action will be executed. This family of LCS algorithms is best suited to multi-step problems, planning, speeding up learning, or disambiguating perceptual aliasing (i.e. where the same observation is obtained in distinct states but requires different actions). Butz later pursued this anticipatory family of LCS developing a number of improvements to the original method.[40] In 2002, Wilson introduced XCSF, adding a computed action in order to perform function approximation.[41] In 2003, Bernado-Mansilla introduced a sUpervised Classifier System (UCS), which specialized the XCS algorithm to the task of supervised learning, single-step problems, and forming a best action set. UCS removed the reinforcement learning strategy in favor of a simple, accuracy-based rule fitness as well as the explore/exploit learning phases, characteristic of many reinforcement learners. Bull introduced a simple accuracy-based LCS (YCS)[42] and a simple strength-based LCS Minimal Classifier System (MCS)[43] in order to develop a better theoretical understanding of the LCS framework. Bacardit introduced GAssist[44] and BioHEL,[45] Pittsburgh-style LCSs designed for data mining and scalability to large datasets in bioinformatics applications. Butz introduced the first rule online learning visualization within a GUI for XCSF[1] (see the image at the top of this page). Urbanowicz extended the UCS framework and introduced ExSTraCS, explicitly designed for supervised learning in noisy problem domains (e.g. epidemiology and bioinformatics).[46] ExSTraCS integrated (1) expert knowledge to drive covering and genetic algorithm towards important features in the data,[47] (2) a form of long-term memory referred to as attribute tracking,[48] allowing for more efficient learning and the characterization of heterogeneous data patterns, and (3) a flexible rule representation similar to Bacardit's mixed discrete-continuous attribute list representation.[49] Both Bacardit and Urbanowicz explored statistical and visualization strategies to interpret LCS rules and perform knowledge discovery for data mining.[50][51] Browne and Iqbal explored the concept of reusing building blocks in the form of code fragments and cyclic graphs and were the first to solve the 135-bit mulitplexer benchmark problem by first learning useful building blocks from simpler multiplexer problems.[52] ExSTraCS 2.0 was later introduced to improve Michigan-style LCS scalability, successfully solving the 135-bit multiplexer benchmark problem for the first time directly.[5] The n-bit multiplexer problem is highly epistatic and heterogeneous, making it a very challenging machine learning task.

Advantages

Disadvantages

Variants

Style

Learning

(meaning that it can learn sequentially from a stream of live incoming data not saved in memory, or ,

Representation

Learning/Credit Assignment

Problem Domains

Techniques

Learning classifier systems can be split into two types depending upon where the genetic algorithm acts. A Pittsburgh-type LCS has a population of separate rule sets, where the genetic algorithm recombines and reproduces the best of these rule sets. In a Michigan-style LCS there is only a single set of rules in a population and the algorithm's action focuses on selecting the best classifiers within that set. Michigan-style LCSs have two main types of fitness definitions: strength-based (e.g. ZCS) and accuracy-based (e.g. XCS). The term "learning classifier system" most often refers to Michigan-style LCSs.

Initially the classifiers or rules were binary, but recent research has expanded this representation to include real-valued, neural network, and functional (S-expression) conditions.[citation needed]

Learning classifier systems are not fully understood remains an area of active research.[citation needed] Despite this, they have been successfully applied in many problem domains.

Terminology

The name, 'Learning Classifier System (LCS)', is a bit misleading since there are many machine learning algorithms that 'learn to classify' (e.g. decision trees, artificial neural networks), but are not LCSs. The term 'rule-based machine learning (RBML)' is useful, as it more clearly captures the essential 'rule-based' component of these systems, but it also generalizes to methods that are not considered to be LCSs (e.g. association rule learning, or artificial immune systems). More general terms such as, 'genetics-based machine learning', and even 'genetic algorithm'[36] have also been applied to refer to what would be more characteristically defined as a learning classifier system. Due to their similarity to genetic algorithms, Pittsburgh-style learning classifier systems are sometimes generically referred to as 'genetic algorithms'. Beyond this, some LCS algorithms, or closely related methods, have been referred to as 'cognitive systems',[13] 'adaptive agents', 'production systems', or generically as a 'classifier system'.[53][54] This variation in terminology contributes to some confusion in the field.

Up until the 2000's nearly all learning classifier system methods were developed with reinforcement learning problems in mind. As a result, the term ‘learning classifier system’ was commonly defined as the combination of ‘trial-and-error’ reinforcement learning with the global search of a genetic algorithm. Interest in supervised learning applications, and even unsupervised learning have since broadened the use and definition of this term.

See also

References

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  • Learning Classifier Systems in a Nutshell - (2016) Go inside a basic LCS algorithm to learn their components and how they work.
  • Urbanowicz, Ryan J.; Moore, Jason H. (January 2009), "Learning Classifier Systems: A Complete Introduction, Review, and Roadmap", J. Artif. Evol. App., 2009, New York, NY, United States: Hindawi Publishing Corp.: 1:1–1:25, doi:10.1155/2009/736398{{citation}}: CS1 maint: unflagged free DOI (link)

Webpages

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