title: Towards a framework for designing full model selection and optimization systems

authors: Sun, Quan

subjects: computer science

abstract: People from a variety of industrial domains are beginning to realise that appropriate use of machine learning techniques for their data mining projects could bring great benefits. End-users now have to face the new problem of how to choose a combination of data processing tools and algorithms for a given dataset. This problem is usually termed the Full Model Selection (FMS) problem. Extended from our previous work [10], in this paper, we introduce a framework for designing FMS algorithms. Under this framework, we propose a novel algorithm combining both genetic algorithms (GA) and particle swarm optimization (PSO) named GPS (which stands for GA-PSO-FMS), in which a GA is used for searching the optimal structure for a data mining solution, and PSO is used for searching optimal parameters for a particular structure instance. Given a classification dataset, GPS outputs a FMS solution as a directed acyclic graph consisting of diverse data mining operators that are available to the problem. Experimental results demonstrate the benefit of the algorithm. We also present, with detailed analysis, two model-tree-based variants for speeding up the GPS algorithm.

text: Towards a framework for designing full model selection and optimization systems People from a variety of industrial domains are beginning to realise that appropriate use of machine learning techniques for their data mining projects could bring great benefits. End-users now have to face the new problem of how to choose a combination of data processing tools and algorithms for a given dataset. This problem is usually termed the Full Model Selection (FMS) problem. Extended from our previous work [10], in this paper, we introduce a framework for designing FMS algorithms. Under this framework, we propose a novel algorithm combining both genetic algorithms (GA) and particle swarm optimization (PSO) named GPS (which stands for GA-PSO-FMS), in which a GA is used for searching the optimal structure for a data mining solution, and PSO is used for searching optimal parameters for a particular structure instance. Given a classification dataset, GPS outputs a FMS solution as a directed acyclic graph consisting of diverse data mining operators that are available to the problem. Experimental results demonstrate the benefit of the algorithm. We also present, with detailed analysis, two model-tree-based variants for speeding up the GPS algorithm.

year: 2013

title: Effects of prompts on the frequency of app use: A stress management app for perinatal mental health

authors: Singh, Sukrita

abstract: E-interventions such as smartphone apps may facilitate an alternative treatment for mental health issues. However, often apps are not used after a certain period of time, which limits the potential effectiveness of app intervention programmes. Addition of prompts in app-based programmes may potentially enhance user engagement with apps. ‘Positively Pregnant’ is a smartphone, self-help, interactive app for pregnant women that aims to reduce anxiety, stress and other related problems during the perinatal period. The primary aim of the current study was to test the effectiveness of prompts to increase the app use and also to determine participants’ satisfaction with this stress management app using two groups (guided and non-guided activity). A two-group experimental study design was used on a sample of 88 participants within the New Zealand population. Participants were randomly assigned to one of two groups. The guided activity messages were sent to the participants in guided activity group once every day for 44 days; however, both (experimental and control) groups received ‘tip of the day’ (a general positive quote) every day. Participants completed a pre-assessment questionnaire at the time of recruitment. Further assessments were completed at 24 weeks gestation, at 36 weeks gestation and at 1-month postnatal period. Data from ‘satisfaction and app use’ questionnaire for 24-week gestation was analyzed for this study. No significant difference was found in regards to frequency of app use between two groups. On average, participants in the guided activity group did not report using more components of the app than the participants in non-guided group. No significant differences were found on satisfaction with the app, or on willingness to recommend the app to a friend. Results of this study suggest that the prompts may not be an effective tool to enhance user engagement with this sort of app; however, technical factors may have affected results in this pilot study; therefore, replication is desirable, may be considering the use of direct measures to determine if they correspond with the reported measures used in the study.

text: Effects of prompts on the frequency of app use: A stress management app for perinatal mental health E-interventions such as smartphone apps may facilitate an alternative treatment for mental health issues. However, often apps are not used after a certain period of time, which limits the potential effectiveness of app intervention programmes. Addition of prompts in app-based programmes may potentially enhance user engagement with apps. ‘Positively Pregnant’ is a smartphone, self-help, interactive app for pregnant women that aims to reduce anxiety, stress and other related problems during the perinatal period. The primary aim of the current study was to test the effectiveness of prompts to increase the app use and also to determine participants’ satisfaction with this stress management app using two groups (guided and non-guided activity). A two-group experimental study design was used on a sample of 88 participants within the New Zealand population. Participants were randomly assigned to one of two groups. The guided activity messages were sent to the participants in guided activity group once every day for 44 days; however, both (experimental and control) groups received ‘tip of the day’ (a general positive quote) every day. Participants completed a pre-assessment questionnaire at the time of recruitment. Further assessments were completed at 24 weeks gestation, at 36 weeks gestation and at 1-month postnatal period. Data from ‘satisfaction and app use’ questionnaire for 24-week gestation was analyzed for this study. No significant difference was found in regards to frequency of app use between two groups. On average, participants in the guided activity group did not report using more components of the app than the participants in non-guided group. No significant differences were found on satisfaction with the app, or on willingness to recommend the app to a friend. Results of this study suggest that the prompts may not be an effective tool to enhance user engagement with this sort of app; however, technical factors may have affected results in this pilot study; therefore, replication is desirable, may be considering the use of direct measures to determine if they correspond with the reported measures used in the study.

year: 2018

title: Traditional Knowledge (TK) Labels Transforming Data Infrastructure to Recognise Indigenous Provenance

authors: Hudson, Maui

subjects: Data

abstract: Associate Professor Maui Hudson, KatieLee Riddle and Dr Janette Hamilton-Pearce introduced the latest developments and applications of the Traditional Knowledge Labels and the Local Contexts Hub with examples of use by researchers in Aotearoa New Zealand.

text: Traditional Knowledge (TK) Labels Transforming Data Infrastructure to Recognise Indigenous Provenance Associate Professor Maui Hudson, KatieLee Riddle and Dr Janette Hamilton-Pearce introduced the latest developments and applications of the Traditional Knowledge Labels and the Local Contexts Hub with examples of use by researchers in Aotearoa New Zealand.

year: 2022-09-27