A Classification Module for Genetic Programming Algorithms in JCLEC
Authors: Alberto Cano, José María Luna, Amelia Zafra, Sebastián Ventura
JMLR 2015 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This paper presents an open source software for researchers and end-users to develop classification algorithms based on GP and G3P models. It is an intuitive and usable tool which extends the JCLEC evolutionary computation library (Ventura et al., 2007). The software presented includes some GP and G3P proposals described in literature, and provides the necessary classes and methods to develop any kind of evolutionary algorithms for solving classification problems easily. [...] Due to the core of this module is JCLEC, before an algorithm is ready to run, it is necessary to carry out a set-up process by using a configuration file as shown in Figure 1. |
| Researcher Affiliation | Academia | Alberto Cano EMAIL Jos e Mar ıa Luna EMAIL Amelia Zafra EMAIL Sebasti an Ventura EMAIL Department of Computer Science and Numerical Analysis Rabanales Campus, University of C ordoba, 14071, C ordoba, Spain |
| Pseudocode | No | The paper describes the module's structure and how algorithms are integrated (e.g., inheriting classes) and refers to existing algorithms in the literature, but it does not present any algorithm in a structured pseudocode block or explicitly labeled 'Algorithm' section. |
| Open Source Code | Yes | The software is written in Java and it is available from http://jclec.sourceforge.net/classification under the GPL license. |
| Open Datasets | Yes | Finally, it is noteworthy that several utility classes, which make it easy to load data from KEEL1 and ARFF2 formatted files, are provided by a dataset package. [...] 1. KEEL website at http://www.keel.es [...] <d a t a s e t type = net . s f . j c l e c . problem . u t i l . d a t a s e t . Arff Data Set > <t r a i n data>data / i r i s / i r i s 10 1t r a . a r f f</ t r a i n data> <t e s t data>data / i r i s / i r i s 10 1 t s t . a r f f</ t e s t data> |
| Dataset Splits | Yes | <d a t a s e t type = net . s f . j c l e c . problem . u t i l . d a t a s e t . Arff Data Set > <t r a i n data>data / i r i s / i r i s 10 1t r a . a r f f</ t r a i n data> <t e s t data>data / i r i s / i r i s 10 1 t s t . a r f f</ t e s t data> <a t t r i b u t e c l a s s name>Class</ a t t r i b u t e c l a s s name> </ d a t a s e t> |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running experiments or developing the software. |
| Software Dependencies | Yes | JCLEC requires Java 1.7, Apache commons logging 1.1, Apache commons collections 3.2, Apache commons configuration 1.5, Apache commons lang 2.4, and JUnit 3.8 (for running tests). |
| Experiment Setup | Yes | From Figure 1, the sample configuration file contains specific parameters: <population s i z e>100</ population s i z e>, <max of g e n e r a t i o n s>100</max of g e n e r a t i o n s>, <max deriv s i z e>20</max deriv s i z e>, <recombination prob>0 . 8</ recombination prob>, <copy prob>0.01</copy prob>. |