title: Scalable Multi-label Classification

authors: Read, Jesse

subjects: multi-label

abstract: Multi-label classification is relevant to many domains, such as text, image and other media, and bioinformatics. Researchers have already noticed that in multi-label data, correlations exist between labels, and a variety of approaches, drawing inspiration from many spheres of machine learning, have been able to model these correlations. However, data sources from the real world are growing ever larger and the multi-label task is particularly sensitive to this due to the complexity associated with multiple labels and the correlations between them. Consequently, many methods do not scale up to large problems. This thesis deals with scalable multi-label classification: methods which exhibit high predictive performance, but are also able to scale up to larger problems. The first major contribution is the pruned sets method, which is able to model label correlations directly for high predictive performance, but reduces overfitting and complexity over related methods by pruning and subsampling label sets, and can thus scale up to larger datasets. The second major contribution is the classifier chains method, which models correlations with a chain of binary classifiers. The use of binary models allows for scalability to even larger datasets. Pruned sets and classifier chains are robust with respect to both the variety and scale of data that they can deal with, and can be incorporated into other methods. In an ensemble scheme, these methods are able to compete with state-of-the-art methods in terms of predictive performance as well as scale up to large datasets of hundreds of thousands of training examples. This thesis also puts a special emphasis on multi-label evaluation; introducing a new evaluation measure and studying threshold calibration. With one of the largest and most varied collections of multi-label datasets in the literature, extensive experimental evaluation shows the advantage of these methods, both in terms of predictive performance, and computational efficiency and scalability.

text: Scalable Multi-label Classification Multi-label classification is relevant to many domains, such as text, image and other media, and bioinformatics. Researchers have already noticed that in multi-label data, correlations exist between labels, and a variety of approaches, drawing inspiration from many spheres of machine learning, have been able to model these correlations. However, data sources from the real world are growing ever larger and the multi-label task is particularly sensitive to this due to the complexity associated with multiple labels and the correlations between them. Consequently, many methods do not scale up to large problems. This thesis deals with scalable multi-label classification: methods which exhibit high predictive performance, but are also able to scale up to larger problems. The first major contribution is the pruned sets method, which is able to model label correlations directly for high predictive performance, but reduces overfitting and complexity over related methods by pruning and subsampling label sets, and can thus scale up to larger datasets. The second major contribution is the classifier chains method, which models correlations with a chain of binary classifiers. The use of binary models allows for scalability to even larger datasets. Pruned sets and classifier chains are robust with respect to both the variety and scale of data that they can deal with, and can be incorporated into other methods. In an ensemble scheme, these methods are able to compete with state-of-the-art methods in terms of predictive performance as well as scale up to large datasets of hundreds of thousands of training examples. This thesis also puts a special emphasis on multi-label evaluation; introducing a new evaluation measure and studying threshold calibration. With one of the largest and most varied collections of multi-label datasets in the literature, extensive experimental evaluation shows the advantage of these methods, both in terms of predictive performance, and computational efficiency and scalability.

year: 2010

title: Direct extrusion 3D printing for a softer PLA-based bio-polymer composite in pellet form

authors: Singamneni, Sarat

subjects: polymer composite

abstract: Fused deposition modelling is the most popular method of 3D printing with a variety of polymers, but the raw materials are commonly in the filament form. Research has demonstrated the ability to use pellets for this process, which are easier to produce than filaments. Apart from the savings on the pre-processing, the approach also combines the good attributes of both injection moulding and 3D printing and referred to as extrusion 3D printing. Direct printing of pellets is especially suited for thermosensitive polymers, with which, repeated or excessive heating may lead to degeneration. Also, polymers that are too soft do not qualify for filament-based extrusion. PLA is a popular choice for fused deposition modelling in the filament form but is often too brittle. A softer version of PLA composite based on PBAT and cellulose fibres is proposed here for 3D printing. Considering the lack of stiffness of the filaments, direct extrusion from pellet form is evaluated. Regardless of form, the polymer material system satisfied the stringent conditions of consolidation as dictated by the dynamic combination of extrusion and rasterised material deposition. Experimental evaluation based on meso-structural and mechanical property analyses indicate the new pellet-based material system to be suitable and to perform well. The novelty of the material and process combination is that the printed samples were actually comparable mechanically to the injection moulded counterparts, which is an extraordinary achievement, considering the shortcomings typical of material consolidation in additive manufacturing.

text: Direct extrusion 3D printing for a softer PLA-based bio-polymer composite in pellet form Fused deposition modelling is the most popular method of 3D printing with a variety of polymers, but the raw materials are commonly in the filament form. Research has demonstrated the ability to use pellets for this process, which are easier to produce than filaments. Apart from the savings on the pre-processing, the approach also combines the good attributes of both injection moulding and 3D printing and referred to as extrusion 3D printing. Direct printing of pellets is especially suited for thermosensitive polymers, with which, repeated or excessive heating may lead to degeneration. Also, polymers that are too soft do not qualify for filament-based extrusion. PLA is a popular choice for fused deposition modelling in the filament form but is often too brittle. A softer version of PLA composite based on PBAT and cellulose fibres is proposed here for 3D printing. Considering the lack of stiffness of the filaments, direct extrusion from pellet form is evaluated. Regardless of form, the polymer material system satisfied the stringent conditions of consolidation as dictated by the dynamic combination of extrusion and rasterised material deposition. Experimental evaluation based on meso-structural and mechanical property analyses indicate the new pellet-based material system to be suitable and to perform well. The novelty of the material and process combination is that the printed samples were actually comparable mechanically to the injection moulded counterparts, which is an extraordinary achievement, considering the shortcomings typical of material consolidation in additive manufacturing.

year: 2021

title: Evidence of swarm intelligence in collective cultures: Identifying the use of the swarm goal directive of productivity in Pacific organisation systems as well as getween genders

authors: Arun, Natalie M.

subjects: Maori

abstract: Intrigued by the existence of societies outside that of the human population, scientists have ventured to study social aggregations within insects to seek insights on effective colonizing. The most popular of these social aggregations are colonies of ants and bees. In studying these groups of social insects researchers have developed algorithms loosely termed swarm intelligence that increase work efficiency within businesses and other social organizations (Bonabeau & Meyer, 2001). A subsequent proliferation of research in surrounding fields has allowed for investigation of key variables that improve work on a global scale (Bonabeau & Meyer, 2001). James Kennedy (1999), an initiator of swarm research, has suggested that there is a high correlation between systems that rely on each other for information and greater task accomplishment.

text: Evidence of swarm intelligence in collective cultures: Identifying the use of the swarm goal directive of productivity in Pacific organisation systems as well as getween genders Intrigued by the existence of societies outside that of the human population, scientists have ventured to study social aggregations within insects to seek insights on effective colonizing. The most popular of these social aggregations are colonies of ants and bees. In studying these groups of social insects researchers have developed algorithms loosely termed swarm intelligence that increase work efficiency within businesses and other social organizations (Bonabeau & Meyer, 2001). A subsequent proliferation of research in surrounding fields has allowed for investigation of key variables that improve work on a global scale (Bonabeau & Meyer, 2001). James Kennedy (1999), an initiator of swarm research, has suggested that there is a high correlation between systems that rely on each other for information and greater task accomplishment.

year: 2008