Evolution of classification rules for comprehensible knowledge discovery

Carreño, E H Pablo; Leguizamón, Guillermo; Wagner, Neal

doi:10.1109/cec.2007.4424615

Cited by 13 publications

(3 citation statements)

References 7 publications

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“…A dynamic pattern recognition method is proposed in [4]. In [5,6], the utilization of evolving classifiers for activity recognition is described. Hybrid and ensemble methods in machine learning are focused on in [7].…”

Section: Introductionmentioning

confidence: 99%

“…Analyzing some methods to build classifiers based on evolution rules as in [5] an electronic nose using a proposed design algorithm as the classifier module into the Arduino is considered to create such a solution. This analysis led to consider a performance comparison among an ATMega 328 microcontroller device versus a computer running the classifier selected with strategies like in [6] for a portable optimal solution.…”

Section: Introductionmentioning

confidence: 99%

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Classification via an Embedded Approach

Rubio

Avila

Melendez

et al. 2017

Designs

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This paper presents the results of an automated volatile organic compound (VOC) classification process implemented by embedding a machine learning algorithm into an Arduino Uno board. An electronic nose prototype is constructed to detect VOCs from three different fruits. The electronic nose is constructed using an array of five tin dioxide (SnO 2 ) gas sensors, an Arduino Uno board used as a data acquisition section, as well as an intelligent classification module by embedding an approach function which receives data signals from the electronic nose. For the intelligent classification module, a training algorithm is also implemented to create the base of a portable, automated, fast-response, and economical electronic nose device. This solution proposes a portable system to identify and classify VOCs without using a personal computer (PC). Results show an acceptable precision for the embedded approach in comparison with the performance of a toolbox used in a PC. This constitutes an embedded solution able to recognize VOCs in a reliable way to create application products for a wide variety of industries, which are able to classify data acquired by an electronic nose, as VOCs. With this proposed and implemented algorithm, a precision of 99% for classification was achieved into the embedded solution.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification via an Embedded Approach

Rubio

Avila

Melendez

et al. 2017

Designs

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“…However, few approaches to evolving classifiers with the decision list representation exist, and those that do contain only limited evaluation, e.g. [3], [4] and [5]. In [6], we proposed a technique, named GEDEL (GEnetic DEcision Lists), for evolving decision lists.…”

Section: Introductionmentioning

confidence: 99%

Evolving accurate and comprehensible classification rules

Sönströd

Johansson

König

2011

2011 IEEE Congress of Evolutionary Computation (CEC)

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Abstract-In this paper, Genetic Programming is used to evolve ordered rule sets (also called decision lists) for a number of benchmark classification problems, with evaluation of both predictive performance and comprehensibility. The main purpose is to compare this approach to the standard decision list algorithm JRip and also to evaluate the use of different length penalties and fitness functions for evolving this type of model. The results, using 25 data sets from the UCI repository, show that genetic decision lists with accuracy-based fitness functions outperform JRip regarding accuracy. Indeed, the best setup was significantly better than JRip. JRip, however, held a slight advantage over these models when evaluating AUC. Furthermore, all genetic decision list setups produced models that were more compact than JRip models, and thus more readily comprehensible. The effect of using different fitness functions was very clear; in essence, models performed best on the evaluation criterion that was used in the fitness function, with a worsening of the performance for other criteria. Brier score fitness provided a middle ground, with acceptable performance on both accuracy and AUC. The main conclusion is that genetic programming solves the task of evolving decision lists very well, but that different length penalties and fitness functions have immediate effects on the results. Thus, these parameters can be used to control the trade-off between different aspects of predictive performance and comprehensibility.

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A Framework for Optimization of Genetic Programming Evolved Classifier Expressions Using Particle Swarm Optimization

Jabeen

Baig

2010

Lecture Notes in Computer Science

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Evolution of classification rules for comprehensible knowledge discovery

Cited by 13 publications

References 7 publications

Classification via an Embedded Approach

Classification via an Embedded Approach

Evolving accurate and comprehensible classification rules

A Framework for Optimization of Genetic Programming Evolved Classifier Expressions Using Particle Swarm Optimization

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