New Classification Methods for Gathering Patterns in the Context of Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@PhdThesis{Garcia-Almanza:thesis,
  author =       "Alma Lilia {Garcia Almanza}",
  title =        "New Classification Methods for Gathering Patterns in
                 the Context of Genetic Programming",
  school =       "Department of Computing and Electronic Systems,
                 University of Essex",
  year =         "2008",
  address =      "Colchester, UK",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.bracil.net/finance/papers/Garcia-PhD2008.pdf",
  size =         "244 pages",
  abstract =     "Machine learning techniques extend the past
                 experiences into the future. However, when the number
                 of examples in the minority class (positive cases) is
                 very small in comparison with the remaining classes, it
                 poses a serious challenge to the machine learning
                 [63],[119],[5],[81]. In this kind of problems, the
                 prediction of the majority class is favoured because it
                 has a high chance of being correct. This characteristic
                 is present in many real-world problems, whose objective
                 is to classify the minority class in imbalanced data
                 sets. However, a prediction that detects more positive
                 cases may be paid for with more false alarms. It is
                 important to determine a balance between the detection
                 of positive cases and false alarms. A range of
                 classifications would give users the option to choose
                 the best tradeoff between detecting positive cases and
                 false alarms according to their requirements. On the
                 other hand, we consider it is important to provide a
                 comprehensive solution, which shows the real variables
                 and conditions in the prediction. Thus, the users could
                 combine their knowledge in order to make a more
                 informed decision.

                 In this thesis, we present three novel approaches:
                 Repository Method (RM), Evolving Decision Rules (EDR)
                 and Scenario Method (SM). We use Genetic Programming
                 (GP) and supervised learning to build the methods
                 proposed in this thesis. The main objectives of RM and
                 EDR are: to predict the minority class in imbalanced
                 environments, to generate a range of solutions to suit
                 different users' preferences and to provide an
                 comprehensible solution for the user. On the other
                 hand, SM has been designed to improve the precision and
                 accuracy of the prediction. However, such improvement
                 is paid for with a decrease in the recall. But, the
                 users have to make the decision of which of these
                 parameters is more adequate to satisfy their
                 needs.

                 This work is illustrated predicting future
                 opportunities in financial stock markets. Experiments
                 of our methods were carried out, and these showed
                 promising results for achieving our goals. RM and EDR
                 were compared to a standard Genetic Programming,
                 EDDIE-Arb and C5.0.

                 The methods presented in this thesis can also be used
                 in other fields of knowledge, these should not be
                 limited to financial forecasting problems.",
}

Genetic Programming entries for Alma Lilia Garcia Almanza

Citations