title: Tree-structured multiclass probability estimators

authors: Leathart, Timothy Matthew

abstract: Nested dichotomies are used as a method of transforming a multiclass classification problem into a series of binary problems. A binary tree structure is constructed over the label space that recursively splits the set of classes into subsets, and a binary classification model learns to discriminate between the two subsets of classes at each node. Several distinct nested dichotomy structures can be built in an ensemble for superior performance. In this thesis, we introduce two new methods for constructing more accurate nested dichotomies. Random-pair selection is a subset selection method that aims to group similar classes together in a non-deterministic fashion to easily enable the construction of accurate ensembles. Multiple subset evaluation takes this, and other subset selection methods, further by evaluating several different splits and choosing the best performing one. Finally, we also discuss the calibration of the probability estimates produced by nested dichotomies. We observe that nested dichotomies systematically produce under-confident predictions, even if the binary classifiers are well calibrated, and especially when the number of classes is high. Furthermore, substantial performance gains can be made when probability calibration methods are also applied to the internal models.

text: Tree-structured multiclass probability estimators Nested dichotomies are used as a method of transforming a multiclass classification problem into a series of binary problems. A binary tree structure is constructed over the label space that recursively splits the set of classes into subsets, and a binary classification model learns to discriminate between the two subsets of classes at each node. Several distinct nested dichotomy structures can be built in an ensemble for superior performance. In this thesis, we introduce two new methods for constructing more accurate nested dichotomies. Random-pair selection is a subset selection method that aims to group similar classes together in a non-deterministic fashion to easily enable the construction of accurate ensembles. Multiple subset evaluation takes this, and other subset selection methods, further by evaluating several different splits and choosing the best performing one. Finally, we also discuss the calibration of the probability estimates produced by nested dichotomies. We observe that nested dichotomies systematically produce under-confident predictions, even if the binary classifiers are well calibrated, and especially when the number of classes is high. Furthermore, substantial performance gains can be made when probability calibration methods are also applied to the internal models.

year: 2019

title: Part-time Employees Were Not Created Equal: Exploring How Part-time Groups Differ on Measures That Can Predict Employee Turnover Intention

authors: Whistler, Hamish Tomas

subjects: part-time

abstract: This study built upon research by Martin and Sinclair (2007) who found the part-time employee group is made up of distinct groups that differ in their responses across a range of attitudinal variables (e.g. measures of job satisfaction and commitment). Part-time employees typically have higher employee turnover rates than full-time employees and this study investigated this issue by collecting data on attitudinal variables known to predict employee turnover intentions. 
The 311 participants of this study were from different branches across a large retail organisation in New Zealand. The results show that the high-school and tertiary student part-time groups often responded to scales similar to each other, while the 'PT (other)' and full-time employee groups were more similar in their responses. These results show a pattern of responses for the part-time group called 'PT (other)' that suggests they are less likely to quit compared to the two student part-time groups. Work status (part-time or full-time) was found to moderate the relationship between fulfilment of psychological contract with both turnover intentions (job search behaviours) and overall satisfaction. This study also developed and tested two new measures expected to relate to employee turnover. The first was pre-planned intention to quit, which successfully predicted differences between groups and had correlations with other scales. The second measure was social network strength, but this did not predict differences between groups or have correlations with other variables. 
	The main implication was that retail organisations should be aware that the student part-time groups are more likely to have a higher turnover rate than other groups. This information might be useful when recruiting for positions where an employee quit has significant negative effect on the operation of the organisation.

text: Part-time Employees Were Not Created Equal: Exploring How Part-time Groups Differ on Measures That Can Predict Employee Turnover Intention This study built upon research by Martin and Sinclair (2007) who found the part-time employee group is made up of distinct groups that differ in their responses across a range of attitudinal variables (e.g. measures of job satisfaction and commitment). Part-time employees typically have higher employee turnover rates than full-time employees and this study investigated this issue by collecting data on attitudinal variables known to predict employee turnover intentions. 
The 311 participants of this study were from different branches across a large retail organisation in New Zealand. The results show that the high-school and tertiary student part-time groups often responded to scales similar to each other, while the 'PT (other)' and full-time employee groups were more similar in their responses. These results show a pattern of responses for the part-time group called 'PT (other)' that suggests they are less likely to quit compared to the two student part-time groups. Work status (part-time or full-time) was found to moderate the relationship between fulfilment of psychological contract with both turnover intentions (job search behaviours) and overall satisfaction. This study also developed and tested two new measures expected to relate to employee turnover. The first was pre-planned intention to quit, which successfully predicted differences between groups and had correlations with other scales. The second measure was social network strength, but this did not predict differences between groups or have correlations with other variables. 
	The main implication was that retail organisations should be aware that the student part-time groups are more likely to have a higher turnover rate than other groups. This information might be useful when recruiting for positions where an employee quit has significant negative effect on the operation of the organisation.

year: 2009

title: Ngā ia nōnoke taketake

authors: Manuirirangi, Hōri

subjects: whātōtō nōnoke mamau

abstract: Tā tēnei tuhinga, he titiro ki ngā kōrero onamata mō ngā mahi nōnoke a te Māori. I mua, whāia ai te nōnoke e ētehi hapori Māori hei koringa tinana, hei tū hākinakina, hei tākaro hoki. Ko te nōnoke, te whātōtō, te taitai me te mamau, he rite katoa. Nō te kuhutanga mai o ngā tini whakaawenga a tauiwi, ka nui hoki te Māori i tahuri ki hākinakina kē. Nāwhai ā ka mimiti te whāia o te nōnoke. Kei tēnei tuhinga, ka āta wetekina ngā kupu me ngā kōrero o te reo nōnoke o mua, arā ngā tohutohu a ngā pāhake e whakaahua ana i ngā tūmomo neke, i ngā whiu me ngā puringa mahia ai e te kaiwhātōtō. Ka arohaehaetia tēnei mea te karakia, te waiata me te pepeha kia kuhuna ai te hinengaro o te kaiwhātōtō e mārama rawa ai te ia o ōna whakaaro, tōna waiaro me tōna whanonga. Waihoki ko ngā pūrākau o mua, kua whakaemia ētehi tauira kia tīroua ngā karere i whaona ki roto. Ko te tino whāinga, ko te whera i ngā rangahau nei kia āta mōhio ki ngā koronga me ngā horopaki whāia ai e te Māori o mua ngā mahi nōnoke.

text: Ngā ia nōnoke taketake Tā tēnei tuhinga, he titiro ki ngā kōrero onamata mō ngā mahi nōnoke a te Māori. I mua, whāia ai te nōnoke e ētehi hapori Māori hei koringa tinana, hei tū hākinakina, hei tākaro hoki. Ko te nōnoke, te whātōtō, te taitai me te mamau, he rite katoa. Nō te kuhutanga mai o ngā tini whakaawenga a tauiwi, ka nui hoki te Māori i tahuri ki hākinakina kē. Nāwhai ā ka mimiti te whāia o te nōnoke. Kei tēnei tuhinga, ka āta wetekina ngā kupu me ngā kōrero o te reo nōnoke o mua, arā ngā tohutohu a ngā pāhake e whakaahua ana i ngā tūmomo neke, i ngā whiu me ngā puringa mahia ai e te kaiwhātōtō. Ka arohaehaetia tēnei mea te karakia, te waiata me te pepeha kia kuhuna ai te hinengaro o te kaiwhātōtō e mārama rawa ai te ia o ōna whakaaro, tōna waiaro me tōna whanonga. Waihoki ko ngā pūrākau o mua, kua whakaemia ētehi tauira kia tīroua ngā karere i whaona ki roto. Ko te tino whāinga, ko te whera i ngā rangahau nei kia āta mōhio ki ngā koronga me ngā horopaki whāia ai e te Māori o mua ngā mahi nōnoke.

year: 2017