Multiple pheromone types and other extensions to the Ant-Miner classification rule discovery algorithm

Salama, Khalid M.; Abdelbar, Ashraf M.; Freitas, Alex A.

doi:10.1007/s11721-011-0057-9

Cited by 48 publications

(25 citation statements)

References 13 publications

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“…On the other hand, Salama et al recently introduced an efficient version of the algorithm, µAnt-Miner [5], [6], based on selecting the consequent class of the rule before constructing its antecedent and utilizing multiple pheromone types, one for each permitted rule class. This motivated the idea of utilizing the pre-selected class in heuristic information calculation, and continuous attribute discretization.…”

Section: Introductionmentioning

confidence: 99%

“…It is recommended for the reader to have a background on these two algorithm in order to understand the foundation of the extensions proposed in the current work [3], [4], [5], [6].…”

Section: Introductionmentioning

confidence: 99%

“…The use of multiple pheromones was introduced in µAnt-Miner [5], [6] as an extension to the original algorithm. The motivation behind the multi-pheromone system is based on the following hypothesis: the selection of the terms (in the rule antecedent) that are relevant to the prediction of a specific class (rule consequent) constructs better rules than selecting terms simply to reduce the entropy among the class distribution on the dataset, as in the original Ant-Miner.…”

Section: Introductionmentioning

confidence: 99%

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Using a unified measure function for heuristics, discretization, and rule quality evaluation in Ant-Miner

Salama

Otero

2013

2013 IEEE Congress on Evolutionary Computation

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Abstract-Ant-Miner is a classification rule discovery algorithm that is based on Ant Colony Optimization (ACO) metaheuristic. cAnt-Miner is the extended version of the algorithm that handles continuous attributes on-the-fly during the rule construction process, while µAnt-Miner is an extension of the algorithm that selects the rule class prior to its construction, and utilizes multiple pheromone types, one for each permitted rule class. In this paper, we combine these two algorithms to derive a new approach for learning classification rules using ACO. The proposed approach is based on using the measure function for 1) computing the heuristics for rule term selection, 2) a criteria for discretizing continuous attributes, and 3) evaluating the quality of the constructed rule for pheromone update as well. We explore the effect of using different measure functions for on the output model in terms of predictive accuracy and model size. Empirical evaluations found that hypothesis of different functions produce different results are acceptable according to Friedman's statistical test.

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Section: Introductionmentioning

confidence: 99%

“…It is recommended for the reader to have a background on these two algorithm in order to understand the foundation of the extensions proposed in the current work [3], [4], [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using a unified measure function for heuristics, discretization, and rule quality evaluation in Ant-Miner

Salama

Otero

2013

2013 IEEE Congress on Evolutionary Computation

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“…The standard deviation of the Impact variable distribution is 33. 126. As we proceed towards slowly evolving populations, the standard deviation decreases, 120 reaching 2.014 in the Fig.…”

Section: Hypothesis Pearson Correlationmentioning

confidence: 85%

“…modeling the pheromone with (depending on the naming convention) scents, flavors or simply levels, however, they still apply their extended algorithms to single resource queries only [110]. Alternatively, they label the classes in a prearranged, rather than a dynamic, manner [126] [13]. In addition, in the majority of the cases the additional pheromone levels are fixed, have predetermined semantical interpretations and are statically incorporated in the classical equations [77].…”

Section: Introductionmentioning

confidence: 99%

Ant Colony Algorithms for the Resolution of Semantic Searches in P2P Networks

Krynicki¹

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SummaryThe long-lasting trend in the field of computation of stress and resource distribution has found its way into computer networks via the concept of peer-to-peer (P2P ) connectivity. P2P is a symmetrical model, where each network node is enabled a comparable range of capacities and resources. It stands in a stark contrast to the classical, strongly asymmetrical client-server approach. P2P, originally considered only a complimentary, server-side structure to the straightforward client-server model, has been shown to have the substantial potential on its own, with multiple, widely known benefits: good fault tolerance and recovery, satisfactory scalability and intrinsic load distribution. However, contrary to client-server, P2P networks require sophisticated solutions on all levels, ranging from network organization, to resource location and managing.In this thesis we address one of the key issues of P2P networks: performing efficient resource searches of semantic nature under realistic, dynamic conditions. There have been numerous solutions to this matter, with evolutionary, stigmergybased, and simple computational foci, but few attempt to resolve the full range of challenges this problem entails. To name a few: real-life P2P networks are rarely static, nodes disconnect, reconnect and change their content. In addition, a trivial incorporation of semantic searches into well-known algorithms causes significant decrease in search efficiency.In our research we build a solution incrementally, starting with the classic Ant Colony System (ACS ) within the Ant Colony Optimization metaheuristic (ACO). ACO is an algorithmic framework used for solving combinatorial optimization problems that fits contractually the problem very well, albeit not providing an immediate solution to any of the aforementioned problems.First, we propose an efficient ACS variant in structured (hypercube structured) P2P networks, by enabling a path-post processing algorithm, which called Tabu Route Optimization (TRO). Next, we proceed to resolve the issue of network dynamism with an ACO-compatible information diffusion approach. Consequently, we attempt to incorporate the semantic component of the searches. This initial approximation to the problem was achieved by allowing ACS to differentiate between iii search types with the pheromone-per-concept idea. We called the outcome of this merger Routing Concept ACS (RC-ACS ). RC-ACS is a robust, static multipheromone implementation of ACS. However, we were able to conclude from it that the pheromone-per-concept approach offers only limited scalability and cannot be considered a global solution.Thus, further progress was made in this respect when we introduced to RC-ACS our novel idea: dynamic pheromone creation, which replaces the static one-to-one assignment. We called the resulting algorithm Angry Ant Framework (AAF ). In AAF new pheromone levels are created as needed and during the search, rather than prior to it. The final step was to enable AAF, not only to create pheromone levels, but to reassi...

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Does the ACO$$\mathbb {_R}$$ Algorithm Benefit from the Use of Crossover?

Abdelbar

Salama

2018

Lecture Notes in Computer Science

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Multiple pheromone types and other extensions to the Ant-Miner classification rule discovery algorithm

Cited by 48 publications

References 13 publications

Using a unified measure function for heuristics, discretization, and rule quality evaluation in Ant-Miner

Using a unified measure function for heuristics, discretization, and rule quality evaluation in Ant-Miner

Ant Colony Algorithms for the Resolution of Semantic Searches in P2P Networks

Does the ACO$$\mathbb {_R}$$ Algorithm Benefit from the Use of Crossover?

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