Multi-label learning by extended multi-tier stacked ensemble method with label correlated feature subset augmentation

Classification is one of the basic and most important operations that can be used in data science and machine learning applications. Multi-label classification is an extension of the multi-class problem where a set of class labels are associated with a particular instance at a time. In a multiclass problem, a single class label is associated with an instance at a time. However, there are many different stacked ensemble methods that have been proposed and because of the complexity associated with the multi-label problems, there is still a lot of scope for improving the prediction accuracy. In this paper, we are proposing the novel extended multi-tier stacked ensemble (EMSTE) method with label correlationby feature subset selection technique and then augmenting those feature subsets while constructing the intermediate dataset for improving the prediction accuracy in the generalization phase of the stacking. The performance effect of the proposed method has been compared with existing methods and showed that our proposed method outperforms the other methods

A triple-random ensemble classification method for mining multi-label data

This paper presents a triple-random ensemble learning method for handling multi-label classification problems. The proposed method integrates and develops the concepts of random subspace, bagging and random k-label sets ensemble learning methods to form an approach to classify multi-label data. It applies the random subspace method to feature space, label space as well as instance space. The devised subsets selection procedure is executed iteratively. Each multi-label classifier is trained using the randomly selected subsets. At the end of the iteration, optimal parameters are selected and the ensemble MLC classifiers are constructed. The proposed method is implemented and its performance compared against that of popular multi-label classification methods. The experimental results reveal that the proposed method outperforms the examined counterparts in most occasions when tested on six small to larger multi-label datasets from different domains. This demonstrates that the developed method possesses general applicability for various multi-label classification problems.<br /

Learning from Imbalanced Multi-label Data Sets by Using Ensemble Strategies

Multi-label classification is an extension of conventional classification in which a single instance can be associated with multiple labels. Problems of this type are ubiquitous in everyday life. Such as, a movie can be categorized as action, crime, and thriller. Most algorithms on multi-label classification learning are designed for balanced data and don’t work well on imbalanced data. On the other hand, in real applications, most datasets are imbalanced. Therefore, we focused to improve multi-label classification performance on imbalanced datasets. In this paper, a state-of-the-art multi-label classification algorithm, which called IBLR_ML, is employed. This algorithm is produced from combination of k-nearest neighbor and logistic regression algorithms. Logistic regression part of this algorithm is combined with two ensemble learning algorithms, Bagging and Boosting. My approach is called IB-ELR. In this paper, for the first time, the ensemble bagging method whit stable learning as the base learner and imbalanced data sets as the training data is examined. Finally, to evaluate the proposed methods; they are implemented in JAVA language. Experimental results show the effectiveness of proposed methods. Keywords: Multi-label classification, Imbalanced data set, Ensemble learning, Stable algorithm, Logistic regression, Bagging, Boostin

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