KohonAnts: A Self-Organizing Ant Algorithm for Clustering and Pattern Classification
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KohonAnts: A Self-Organizing Ant Algorithm for Clustering and Pattern Classification



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This is the presentation of KohonAnts (KANTS), an hybrid Ant Colony and Self-organizing Map algorithm for clustering and pattern classification.

Esta es la presentación de KohonAnts (KANTS), un algoritmo que combina conceptos de los algortimos de hormigas y los mapas autoorganizativos y que se puede utilizar para agrupamiento (clustering) o clasificación de patrones.



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    Thanks for your interest. ;)

    Look at the new url, I have change the name of the file and the directory where it is. :)
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    While we fix some troubles in rediris version. ;)
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KohonAnts: A Self-Organizing Ant Algorithm for Clustering and Pattern Classification KohonAnts: A Self-Organizing Ant Algorithm for Clustering and Pattern Classification Presentation Transcript

  • KohonAnts: A Self-Organizing Ant Algorithm for Clustering and Pattern Classification Antonio M. Mora , C.M. Fernandes, J.J. Merelo, J.L.J. Laredo Dpto. ATC. UNIVERSITY OF GRANADA (Spain) V. Ramos, A.C. Rosa LASEEB-ISR/IST. UNIVERSITY OF LISBON (Portugal) ALIFE XI
    • Ant Algorithms
    • Kohonen’s Self Organizing Maps
    • KohonAnts
    • Experiments and Results
    • Conclusions and Future Work
  • Ant Algorithms Natural Ants ALIFE XI
    • Ants live in colonies and cooperate searching for the benefit of the colony instead of their own.
    • Ants move following (smelling) and depositing a chemical substance known as pheromone (which is also evaporated). This ‘comunication’ method is known as stigmergy .
    • They can find the shortest path between their nest and the food location.
    • They build clusters with their larvae and also with their dead bodies.
  • Ant Algorithms Main Features ALIFE XI
    • There are many agents called (artificial) ants .
    • All of them move in a graph following and depositing (artificial) pheromone .
    • They cooperate to find a solution (usually every ant yields a complete solution).
    • There are some formulae applied in the running:
      • state transition rule  decides the next step for each ant
      • pheromone updating  contribution and evaporation
      • evaluation function  assigns the cost to every solution
  • Ant Algorithms Ant System Model ALIFE XI
    • Introduced by Chialvo and Millonas in 1995.
    • Ants move in a grid . Each one is represented by its position r , and orientation to move  .
    • They decide where to move without consider their previous movements.
    • They apply the so-called response function , which depends on 3 parameters:
      •   pheromone density of the cell
      •   degree of randomness with which an ant follow the gradient of pheromone
      •   factor of ant’s ability to sense pheromone
  • Kohonen’s SOM
    • It is an unsupervised Artificial Neural Network.
    • There is only an input and an output layer.
    • There is a Competitive learning (only one neuron is activated for an input pattern).
    • It Maps high-dimensional data into a two-dimensional representation space (usually rectangular or hexagonal grid).
    • After training phase, neurons with similar weights tend to cluster on the map .
    Main Features ALIFE XI
  • Kohonen’s SOM
    • N neurons in the input layer, M neurons in the output layer. Each input neuron is connected with all the output ones. Every input sample is associated to an input neuron.
    • Each neuron of the output layer has associated a weight vector Wj , as the same dimension as the input data.
    General Structure ALIFE XI
  • Kohonen’s SOM
    • For each input pattern/sample , the most similar neuron (considering its weight vector), and its neighbours , are activated.
    Working ALIFE XI
    • The weight vector of every one of these neurons is updated to be closer to the input values.
    • The SOM evolves , ‘moving’ the neurons in order to fit to the input space (of samples).
  • KohonAnts Presentation ALIFE XI
    • It merges Ant System with Kohonen’s SOM concepts.
    • It is a clustering and classification algorithm.
    • Each input sample (vector of variables) is associated to an ant .
    • They move in a toroidal grid which has a vector (of the same dimension) associated to each cell ( pheromone ) .
  • KohonAnts Working ALIFE XI Initialization Decide Where to Go For each Ant: Pheromone Update Pheromone Evaporation END Stop criteria is TRUE No more Ants Stop criteria is FALSE
  • KohonAnts Main Functions (I) ALIFE XI
    • Decide Where to Go
      • every ant evaluates its neighbourhood (similar to SOM) to find the cell where move to. A probability is associated to every cell .
      • the probability of each cell depends on the euclidean distance between the ant vector and the centroid of a zone surrounding the cell.
    o o o o
    • yellow circle is the neighbourhood (all possible cells to move) with radius = 2.
    • green circles are areas to calculate centroid (marked with ’o’).
    • the centroid is the arithmetic mean of the vectors inside the circle.
    • So, every ant moves with higher probability to cells surrounded by vectors similar to its own .
  • KohonAnts Main Functions (II) ALIFE XI
    • Pheromone Update
      • once an ant chooses the cell to move to (inside its neighbourhood), it updates the vector of that cell and makes it closer to its own vector .
      • it applies a formula similar to the learning function of SOM, so it depends on a learning rate  .
    • Pheromone Evaporation
      • once all the ants have moved, the pheromone tends to disappear (the vectors revert to its initial values).
      • it uses a formula similar to the evaporation function of Ant Algorithms, which depends on an evaporation rate  .
  • KohonAnts Key Features ALIFE XI
    • The neighbourhood radius and the learning rate decreases with the iterations (as in SOM). It means big changes at the beginning ( clustering ) and small changes the rest of the running ( refinement ).
    • An ant can ‘jump’ or ‘fly’, since it can move to cells far than one hop .
    • The pheromone changes the grid (environment) and makes it closer to the ant’s vector (input pattern/sample).
    • The ants tend to form clusters near other similar ants (similar vectors).
    • The environment (the grid) also ‘evolves’ and can be used as a classification tool .
  • Experiments Datasets ALIFE XI
    • IRIS
      • data of 3 species of Iris plant, 50 samples of each class, and 4 variables per sample (flower attributes).
    • GLASS
      • data of 6 classes of glass, 214 samples in total, and 9 variables per sample (chemical composition).
    • PIMA
      • data of 2 classes of diabetes patients, 768 samples , and 8 variables per sample (medical data).
  • Experiments Clustering (Study) ALIFE XI
    • performed over IRIS plants dataset (150 samples, 3 classes)
    • it shows ants’ final position in the grid after 100 iterations
    • a clear line of behaviour can be marked depending on  and 
    • it can be seen that clear clusters are formed for some values of these parameters
  • Experiments Clustering (Results) ALIFE XI
    • performed over IRIS plants dataset (150 samples, 3 classes)
    • it shows the evolution of the clusters of ants
    • in a few iterations, some clusters appear
    • then, they are improved/refined in the following iterations
    Initial situation Iteration 10 Iteration 40 Iteration 100
  • Experiments Classification ALIFE XI
    • example performed over IRIS plants dataset (150 samples, 3 classes)
    • this is the final situation of the grid pheromone. Each cell is associated to a class
    • we apply the KNN method to classify, so the class associated to a sample is the correspondent to the (majority) of the K closest vectors (of the grid)
  • Experiments Classification (Results) ALIFE XI
    • Every dataset has been transformed into 6:
      • 3 of 50% training/50% test
      • 3 of 90% training/10% test
    • The results beat the classical KNN method
    • In recent works also beat other methods such as Neural Networks, Fuzzy Logic, Linear Discriminant Analysis …
  • Conclusions And Future Wok ALIFE XI
    • We have presented a new algorithm which combines ideas of two soft-computing methods such as Kohonen’s Self-Organizing Maps and Ant System Model .
    • It can be used as a clustering and as a pattern classification method.
    • The preliminary results yielded are very promising and beat some other techniques .
    • Make a comparison with some clustering algorithms and with other classification ones. Try more difficult problems.
    • Implement and test some other ideas used in SOMs and AS which may be improve the algorithm. Such a neighbourhood updating, a stop criteria or weights in the decide where to go function.
  • Thank You! ALIFE XI The sources, bin and data can be found in: https://forja.rediris.es/websvn/wsvn/geneura/KohonAnts/