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: Exploring verbal, visual and schematic learners' static and dynamic mental images of scientific species and processes in relation to their spatial ability

authors: Al-Balushi, Sulaiman M.

subjects: appearing/disappearing dynamic images

abstract: The current study compared different learners' static and dynamic mental images of unseen scientific species and processes in relation to their spatial ability. Learners were classified into verbal, visual and schematic. Dynamic images were classified into: appearing/disappearing, linear-movement, and rotation. Two types of scientific entities and their related processes were investigated: astronomical and microscopic. The sample included 79 female students from Grades 9 and 10. For the purpose of the study, three instruments were used. The Mental Images by Guided Imagery instrument was designed to investigate participants' visualization of static and dynamic mental images. The Water-Level Task was adopted to estimate participants' spatial ability. The Learning Styles Inventory was used to classify participants into verbal, visual and schematic learners. The research findings suggest that schematic learners outperformed verbal and visual learners in their spatial ability. They also outperformed them in their vividness of microscopic images; both micro-static and micro-dynamic images; especially in the case of appearing/disappearing images. The differences were not significant in the case of astronomical images. The results also indicate that appearing/disappearing images received the least vividness scores for all three types of learners.

text: Exploring verbal, visual and schematic learners' static and dynamic mental images of scientific species and processes in relation to their spatial ability The current study compared different learners' static and dynamic mental images of unseen scientific species and processes in relation to their spatial ability. Learners were classified into verbal, visual and schematic. Dynamic images were classified into: appearing/disappearing, linear-movement, and rotation. Two types of scientific entities and their related processes were investigated: astronomical and microscopic. The sample included 79 female students from Grades 9 and 10. For the purpose of the study, three instruments were used. The Mental Images by Guided Imagery instrument was designed to investigate participants' visualization of static and dynamic mental images. The Water-Level Task was adopted to estimate participants' spatial ability. The Learning Styles Inventory was used to classify participants into verbal, visual and schematic learners. The research findings suggest that schematic learners outperformed verbal and visual learners in their spatial ability. They also outperformed them in their vividness of microscopic images; both micro-static and micro-dynamic images; especially in the case of appearing/disappearing images. The differences were not significant in the case of astronomical images. The results also indicate that appearing/disappearing images received the least vividness scores for all three types of learners.

year: 2013

title: Lavinia and Henry Dunbar Johnson

authors: Hart, Philip

abstract: Rawinia Manukau, of Ngati Tamatera, married Henry Dunbar Johnson in 1868, when aged 21. Johnson had been a storekeeper at Coromandel from 1863 onwards and after 1866 had the first store at the site of the future Paeroa. In both places, but particularly in the latter, there was always the fear of conflict with the local Maori population, despite his being protected by a rangatira. After spending time at the new Thames goldfield, from 1871 onwards he was a partner in another Paeroa store, being able to erect a house because a rangatira related to his wife wished her to settle there. Lavinia (as she was known to Pakeha) obtained interests in several blocks of land in Ohinemuri, and her husband also acquired some land for a farm. He prospected Karangahake mountain from 1866 onwards, despite Maori opposition, and in 1875 and the following year actively mined there, unprofitably; Lavinia was not involved with this field, but did acquire interests in two claims when the Te Aroha one opened.
A petty squabble with a Thames neighbour resulted in Lavinia telling the latter to go back to England – she was feisty in defending her heritage. Through his marriage and close contact with Maori, Johnson understood and admired the Maori language, leading to his being appointed a licensed interpreter in 1872. After being a leading pioneer of Paeroa, in 1879 he went to Wellington to work in the Native Office, leaving his wife and family behind for a while; after then she was employed by Pakeha as a nurse and midwife. 
In 1885 Johnson was appointed to oversee the development of Rotorua, where he attempted to have Maori children educated (as his own were) and had to cope with the aftermath of the 1886 Tarawera eruption. Retrenched in 1888, he farmed for a while on his wife’s land at Te Aroha West, becoming involved in local issues and local politics. During the 1890 he obtained more official positions, and from 1896 to 1906 was a land court judge.
The Johnson family was well integrated into the dominant culture, all his daughters marrying Pakeha apart from one who married a ‘half-caste’ who had been brought up as a Pakeha. Johnson was not a Pakeha Maori in the original sense, but was accepted by Maori when living in Paeroa and as a judge tried to be kind to poverty-stricken Maori, although in time he viewed Maori as becoming lazy compared with those he had lived amongst during his first two decades in New Zealand.

text: Lavinia and Henry Dunbar Johnson Rawinia Manukau, of Ngati Tamatera, married Henry Dunbar Johnson in 1868, when aged 21. Johnson had been a storekeeper at Coromandel from 1863 onwards and after 1866 had the first store at the site of the future Paeroa. In both places, but particularly in the latter, there was always the fear of conflict with the local Maori population, despite his being protected by a rangatira. After spending time at the new Thames goldfield, from 1871 onwards he was a partner in another Paeroa store, being able to erect a house because a rangatira related to his wife wished her to settle there. Lavinia (as she was known to Pakeha) obtained interests in several blocks of land in Ohinemuri, and her husband also acquired some land for a farm. He prospected Karangahake mountain from 1866 onwards, despite Maori opposition, and in 1875 and the following year actively mined there, unprofitably; Lavinia was not involved with this field, but did acquire interests in two claims when the Te Aroha one opened.
A petty squabble with a Thames neighbour resulted in Lavinia telling the latter to go back to England – she was feisty in defending her heritage. Through his marriage and close contact with Maori, Johnson understood and admired the Maori language, leading to his being appointed a licensed interpreter in 1872. After being a leading pioneer of Paeroa, in 1879 he went to Wellington to work in the Native Office, leaving his wife and family behind for a while; after then she was employed by Pakeha as a nurse and midwife. 
In 1885 Johnson was appointed to oversee the development of Rotorua, where he attempted to have Maori children educated (as his own were) and had to cope with the aftermath of the 1886 Tarawera eruption. Retrenched in 1888, he farmed for a while on his wife’s land at Te Aroha West, becoming involved in local issues and local politics. During the 1890 he obtained more official positions, and from 1896 to 1906 was a land court judge.
The Johnson family was well integrated into the dominant culture, all his daughters marrying Pakeha apart from one who married a ‘half-caste’ who had been brought up as a Pakeha. Johnson was not a Pakeha Maori in the original sense, but was accepted by Maori when living in Paeroa and as a judge tried to be kind to poverty-stricken Maori, although in time he viewed Maori as becoming lazy compared with those he had lived amongst during his first two decades in New Zealand.

year: 2016