8 research outputs found
Graph based Label Enhancement for Multi-instance Multi-label learning
Multi-instance multi-label (MIML) learning is widely applicated in numerous
domains, such as the image classification where one image contains multiple
instances correlated with multiple logic labels simultaneously. The related
labels in existing MIML are all assumed as logical labels with equal
significance. However, in practical applications in MIML, significance of each
label for multiple instances per bag (such as an image) is significant
different. Ignoring labeling significance will greatly lose the semantic
information of the object, so that MIML is not applicable in complex scenes
with a poor learning performance. To this end, this paper proposed a novel MIML
framework based on graph label enhancement, namely GLEMIML, to improve the
classification performance of MIML by leveraging label significance. GLEMIML
first recognizes the correlations among instances by establishing the graph and
then migrates the implicit information mined from the feature space to the
label space via nonlinear mapping, thus recovering the label significance.
Finally, GLEMIML is trained on the enhanced data through matching and
interaction mechanisms. GLEMIML (AvgRank: 1.44) can effectively improve the
performance of MIML by mining the label distribution mechanism and show better
results than the SOTA method (AvgRank: 2.92) on multiple benchmark datasets.Comment: 7 pages,2 figure
Una librería para el aprendizaje multi-instancia multi-etiqueta
Premio extraordinario de Trabajo Fin de Máster curso 2019/2020. Máster en Ingeniería InformáticaThis project presents a library to work on solving multi instance multi label classification problems. It describes the data format, the software architecture, as well as the different algorithmic proposals that it incorporates. The library allows to add new algorithms in a simple way, facilitating researchers in this area to develop, test and compare new proposals. In addition, it is free and open source and is implemented in Java, using the Weka and Mulan libraries. This way, users who work with these libraries in learning with multiple instances and in learning with multiple labels will find a familiar development environment.Este proyecto presenta una librería para trabajar en la resolución de problemas de clasificación con múltiples instancias y múltiples etiquetas. Se describe el formato de datos, la arquitectura software, así como las diferentes propuestas algorítmicas que incorpora. La librería permite añadir nuevos algoritmos de forma sencilla, facilitando a los investigadores en esta área el desarrollo, prueba y comparación de nuevas propuestas. Además, es libre y de código abierto y está implementada en Java, usando las librerías Weka y Mulan. De este modo, los usuarios habituados a trabajar en las librerías anteriores tanto en el aprendizaje con múltiples instancias como en el aprendizaje con múltiples etiquetas, respectivamente, se encontrarán con un entorno de desarrollo con el que están familiarizados