52,985 research outputs found
A lexicographic multi-objective genetic algorithm for multi-label correlation-based feature selection
This paper proposes a new Lexicographic multi-objective Genetic Algorithm for Multi-Label Correlation-based Feature Selection (LexGA-ML-CFS), which is an extension of the previous single-objective Genetic Algorithm for Multi-label Correlation-based Feature Selection (GA-ML-CFS). This extension uses a LexGA as a global search method for generating candidate feature subsets. In our experiments, we compare the results obtained by LexGA-ML-CFS with the results obtained by the original hill climbing-based ML-CFS, the single-objective GA-ML-CFS and a baseline Binary Relevance method, using ML-kNN as the multi-label classifier. The results from our experiments show that LexGA-ML-CFS improved predictive accuracy, by comparison with other methods, in some cases, but in general there was no statistically significant different between the results of LexGA-ML-CFS and other methods
Searching the solution space in constructive geometric constraint solving with genetic algorithms
Geometric problems defined by constraints have an exponential number
of solution instances in the number of geometric elements involved.
Generally, the user is only interested in one instance such that
besides fulfilling the geometric constraints, exhibits some additional
properties.
Selecting a solution instance amounts to selecting a given root every
time the geometric constraint solver needs to compute the zeros of a
multi valuated function. The problem of selecting a given root is
known as the Root Identification Problem.
In this paper we present a new technique to solve the root
identification problem. The technique is based on an automatic search
in the space of solutions performed by a genetic algorithm. The user
specifies the solution of interest by defining a set of additional
constraints on the geometric elements which drive the search of the
genetic algorithm. The method is extended with a sequential niche
technique to compute multiple solutions. A number of case studies
illustrate the performance of the method.Postprint (published version
On the design of an ECOC-compliant genetic algorithm
Genetic Algorithms (GA) have been previously applied to Error-Correcting Output Codes (ECOC) in state-of-the-art works in order to find a suitable coding matrix. Nevertheless, none of the presented techniques directly take into account the properties of the ECOC matrix. As a result the considered search space is unnecessarily large. In this paper, a novel Genetic strategy to optimize the ECOC coding step is presented. This novel strategy redefines the usual crossover and mutation operators in order to take into account the theoretical properties of the ECOC framework. Thus, it reduces the search space and lets the algorithm to converge faster. In addition, a novel operator that is able to enlarge the code in a smart way is introduced. The novel methodology is tested on several UCI datasets and four challenging computer vision problems. Furthermore, the analysis of the results done in terms of performance, code length and number of Support Vectors shows that the optimization process is able to find very efficient codes, in terms of the trade-off between classification performance and the number of classifiers. Finally, classification performance per dichotomizer results shows that the novel proposal is able to obtain similar or even better results while defining a more compact number of dichotomies and SVs compared to state-of-the-art approaches
Feature Selection of Post-Graduation Income of College Students in the United States
This study investigated the most important attributes of the 6-year
post-graduation income of college graduates who used financial aid during their
time at college in the United States. The latest data released by the United
States Department of Education was used. Specifically, 1,429 cohorts of
graduates from three years (2001, 2003, and 2005) were included in the data
analysis. Three attribute selection methods, including filter methods, forward
selection, and Genetic Algorithm, were applied to the attribute selection from
30 relevant attributes. Five groups of machine learning algorithms were applied
to the dataset for classification using the best selected attribute subsets.
Based on our findings, we discuss the role of neighborhood professional degree
attainment, parental income, SAT scores, and family college education in
post-graduation incomes and the implications for social stratification.Comment: 14 pages, 6 tables, 3 figure
Predicting regression test failures using genetic algorithm-selected dynamic performance analysis metrics
A novel framework for predicting regression test failures is proposed. The basic principle embodied in the framework is to use performance analysis tools to capture the runtime behaviour of a program as it executes each test in a regression suite. The performance information is then used to build a dynamically predictive model of test outcomes. Our framework is evaluated using a genetic algorithm for dynamic metric selection in combination with state-of-the-art machine learning classifiers. We show that if a program is modified and some tests subsequently fail, then it is possible to predict with considerable accuracy which of the remaining tests will also fail which can be used to help prioritise tests in time constrained testing environments
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