**Author details**

12 Will-be-set-by-IN-TECH

*K*-Fold cross-validation Success rate (%) Hold-Out cross-validation Success rate (%)

**Table 3.** Results obtained for different Ks of a K-Fold cross-validation and procedure using wavelet db1

*K*-Fold cross-validation Success rate (%) Hold-out cross-validation Success rate (%)

**Table 4.** Results obtained for different Ks of a K-Fold cross-validation procedure using wavelet bior3.7

*K*-Fold cross-validation Success rate (%) Hold-Out cross-validation Success rate (%)

**Table 5.** Results obtained for different Ks of a K-Fold cross-validation procedure using wavelet dmey

all available training data.

euristics, while families *db1*, *bior3.7* and *dmey* were tested for the DWT. Finally, the resulting characteristics were classified using an LS-SVM. In this case, regularization and kernel parameters were automatically optimized by the system dividing the training samples in training and validation sets and retraining the system with the optimal configuration using

The results confirmed the improvement compared to [11], where only three species (versus the four species used in thius work) were classified with a maximum success rate of 95%. Thus, tables 2, 3, 4 and 5 show that the new system reached performance of around 99% on K-Fold cross-validation and 98% on Hold-Out cross- validation. Moreover, the obtained standard deviation was significantly low, although, as expected, slightly higher on Hold-Out

 98.62% ± 0.36 50 97.70% ± 2.10 98.30% ± 0.50 40 97.12% ± 1.54 98.55% ± 0.25 30 95.66% ± 2.29 97.89% ± 0.35 20 94.77% ± 2.28 - - 10 89.14% ± 6.28

(*K*) (% of training samples)

 99.40% ± 0.53 50 98.42% ± 1.74 99.31% ± 0.27 40 96.89% ± 1.93 99.18% ± 0.31 30 96.26% ± 2.40 98.47% ± 0.12 20 94.38% ± 2.89 - - 10 91.08% ± 4.28

(*K*) (% of training samples)

 99.40% ± 0.53 50 98.42% ± 1.74 99.31% ± 0.27 40 96.89% ± 1.93 99.18% ± 0.31 30 96.26% ± 2.40 98.47% ± 0.12 20 94.38% ± 2.89 - - 10 91.08% ± 4.28

(*K*) (% of training samples)

Ticay-Rivas Jaime R., del Pozo-Baños Marcos, Gutiérrez-Ramos Miguel A., Travieso Carlos M. and Jesús B. Alonso

*Signals and Communications Department, Institute for Technological Development and Innovation in Communications, University of Las Palmas de Gran Canaria, Campus University of Tafira, 35017, Las Palmas de Gran Canaria, Las Palmas, Spain*

#### Eberhard William G.

*Smithsonian Tropical Research Institute and Escuela de Biologia Universidad de Costa Rica, Ciudad Universitaria, Costa Rica*

#### **8. References**

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**Ecosystem, Social and Humanity Sciences** 


**Ecosystem, Social and Humanity Sciences** 

14 Will-be-set-by-IN-TECH

[7] Eberhard,W.G., Behavioral Characters for the Higher Classification of Orb-Weaving Spiders, Evolution, Vol. 36, No. 5 (Sep., 1982), pp. 1067-1095, Society for the Study of

[8] Eberhard,W.G., Early Stages of Orb Construction by Philoponella Vicina, Leucauge Mariana, and Nephila Clavipes (Araneae, Uloboridae and Tetragnathidae), and Their Phylogenetic Implications, Journal of Arachnology, Vol. 18, No. 2 (Summer, 1990), pp.

[9] Eberhard,W.G., Computer Simulation of Orb-Web Construction , J American Zoologist ,

[10] Suresh, P. B., Zschokke, S., A computerised method to observe spider web building behaviour in a semi-natural light environment. 19th European colloquium of

[11] Ticay-Rivas, Jaime R.; del Pozo-Baños, Marcos; Eberhard, William G.; Alonso, Jesús B.; Travieso, Carlos; Spider Recognition by Biometric Web Analysis. IWINAC 2011, Part II,

[12] Jing Hu; Si, J.; Olson, B.P.; Jiping He; , "Feature detection in motor cortical spikes by principal component analysis," Neural Systems and Rehabilitation Engineering, IEEE

[13] Qingfu Zhang; Yiu Wing Leung; , "A class of learning algorithms for principal component analysis and minor component analysis," Neural Networks, IEEE Transactions on ,

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[15] Haibo Yao; Lei Tian; , "A genetic-algorithm-based selective principal component analysis (GA-SPCA) method for high-dimensional data feature extraction," Geoscience and

[17] V. Vapnik, "The Nature of Statistical learning Theory." Springer Verlag, New York, 1995. [18] Vojislav Kevman. "Learning and Soft Computing: Support Vector Machines, Neural

[19] B. Schölkopf y A.J. Smola. "Learning with Kernels. Support Vector Machines, Regularization, Optimization, and Beyond", Published by The MIT Press, 2002 . [20] J.A.K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, J. Vandewalle, "Least Squares Support Vector Machines", World Scientific, Singapore, 2002 (ISBN 981-238-151-1) [21] Lim, Jae S., Two-Dimensional Signal and Image Processing, Englewood Cliffs, NJ,

[22] http://www.unesco.org/new/en/natural-sciences/special-themes/biodiversity-initiative/.

[23] Ahmed, N. Natarajan, T. Rao, K.R "Discrete Cosine Transform", IEEE transactions on

[24] Mallat, S., "A theory for multiresolution signal decomposition: the wavelet representation", IEEE Pattern Analysis and Machine Intelligence, Vol. 11, no. 7, pp.

[25] Otsu , N.; "A threshold selection method from gray-level histograms". IEEE Trans. Sys.,

Remote Sensing, IEEE Transactions on , vol.41, no.6, pp. 1469- 1478, June 2003. [16] Nan Liu; Han Wang; , "Feature Extraction with Genetic Algorithms Based Nonlinear Principal Component Analysis for Face Recognition," Pattern Recognition, 2006. ICPR

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**Chapter 8** 

© 2012 Hufnagel et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 Hufnagel et al., licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**Genus Lists of Oribatid Mites –** 

**Change Indication in Research** 

V. Gergócs, R. Homoródi and L. Hufnagel

Additional information is available at the end of the chapter

In most habitats oribatid mites account for the biggest part of microarthropods (e.g. Schenker, 1986, Johnston and Crossley, 2002). They can be found in most terrestrial microhabitats: in soil, leaf litter, moss, underwood, foliage and in aquatic habitats as well (Behan-Pelletier, 1999). They can be found mostly in great species richness and abundance in their habitats (Behan-Pelletier, 1999). They play a significant role in decomposition processes because they fragment the organic matter and influence the biomass and species composition of fungi and bacteria (Wallwork, 1983; Seastedt, 1984; Yoshida and Hijii, 2005). As this group plays a significant role in soil processes, it is necessary to get to know its spatial pattern and the causes of pattern generation, which can be used later for indication

Applicability of Oribatid mites as an indicator group has been emphasized by researchers for several decades. These organisms possess such kind of extraordinary characteristics by which (considered even separately or as a whole) they are able to indicate different changes in their environment. These characteristics have been summarized in several reviews, most thoroughly in the works of Lebrun and van Straalen (1995), Behan-Pelletier (1999) and

Oribatid mites can be found in almost every kind of habitats worldwide: on land, water and most importantly in the layers of soil containing organic materials, but they also conquered several other kind of microhabitats (e. g. lichen, moss, treebark etc.). Apart from the diversity of habitats, their excessive adaptational ability is also shown by great abundance and species richness. In most habitats, they constitute the largest proportion of

http://dx.doi.org/10.5772/48545

**1. Introduction** 

(Behan-Pelletier, 1999).

Gulvik (2007).

**A Unique Perspective of Climate** 

**Chapter 8** 
