Komparasi Dan Analisis Kinerja Model Algoritma SVM Dan PSO-SVM (Studi Kasus Klasifikasi Jalur Minat SMA)

Attribute Selection is very important for classification process. This research has been done by doing attribute selection using PSO method (Particle Swarm Optimization) on SVM algorithm (Support Vector Machine). The development of the classification model uses three parameters especially data attribute, influence of the transformation of various kernel function and penalty factor (C) toward the performance of SVM and PSO-SVM classification. The analysis uses five kernels in mySVM library that existed in Rapidminer application namely dot, radial, polynomial, neural, and anova kernel. The training data used in the first model classification development is student interest data at ABC high school on 2013-2014 year academic. The first model is evaluated using accuracy, precision, recall, and auc value test. The first result shows that the anova kernel on PSO-SVM is able to work with accuracy level 99.30% using penalty factor 0.1. The second model has been developed to predict student interest in XYZ high school. The second result shows that PSO-SVM with kernel anova is able to classify students interest with 99.29% accuracy level. Keywords— Optimization, SVM, PSO-SVM, Student Interest

Effects of different intracranial volume correction methods on univariate sex differences in grey matter volume and multivariate sex prediction

Sex differences in 116 local gray matter volumes (GMVOL) were assessed in 444 males and 444 females without correcting for total intracranial volume (TIV) or after adjusting the data with the scaling, proportions, power‑corrected proportions (PCP), and residuals methods. The results confirmed that only the residuals and PCP methods completely eliminate TIV‑variation and result in sex‑differences that are “small” (∣d∣ 80%) when using raw local GMVOL, but also when using scaling or proportions adjusted‑data or TIV as a single predictor. Conversely, after properly controlling TIV variation with the PCP and residuals’ methods, prediction accuracy dropped to ≈ 60%. It is concluded that gross morphological differences account for most of the univariate and multivariate sex differences in GMVO

Improved support vector machine generalization using normalized input space

Data pre-processing always plays a key role in learning algorithm performance. In this research we consider data pre-processing by normalization for Support Vector Machines (SVMs). We examine the normalization affect across 112 classification problems with SVM using the rbf kernel. We observe a significant classification improvement due to normalization. Finally we suggest a rule based method to find when normalization is necessary for a specific classification problem. The best normalization method is also automatically selected by SVM itself

Improved support vector machine generalization using normalized input space

Data pre-processing always plays a key role in learning algorithm performance. In this research we consider data pre-processing by normalization for Support Vector Machines (SVMs). We examine the normalization affect across 112 classification problems with SVM using the rbf kernel. We observe a significant classification improvement due to normalization. Finally we suggest a rule based method to find when normalization is necessary for a specific classification problem. The best normalization method is also automatically selected by SVM itself

Mapeo remoto multisensor de coberturas agrícolas extensivas en la zona central de Córdoba

En la presente tesis se describe el uso práctico de la teledetección en el mapeo de cultivos. Los objetivos fueron: Evaluar y comparar el desempeño de algoritmos de clasificación; Determinar la posibilidad de extrapolar firmas espectrales; y Caracterizar cubiertas de residuos agrícolas con datos SAR. El estudio fue desarrollado en el área central de la Provincia de Córdoba, Argentina. Se utilizaron imágenes de sensores pasivos y activos. La precisión de las clasificaciones fue evaluada mediante matrices de confusión. Empleando datos ópticos, las mejores precisiones se alcanzaron al utilizar clases que representaban los diversos estadios fenológicos de cada cultivo. La clasificación realizada con datos inter-anuales, evidenció muy buenas precisiones. El análisis de datos de radar sobre residuos de cultivos, determinó que el filtro Lee es el más apropiado para el problema en estudio. La polarización HH permitió una mejor discriminación de las clases