title: Investigating the data in investor alerting portals

authors: Nichols, David M.

subjects: computer science

abstract: Financial regulators in many jurisdictions publish investor alerts that identify newly identified threats such as imposter websites and unlicensed firms. Many of these lists are aggregated by the International Organization of Securities Commissions. We analyse these alerts to understand if they can be used to create cybersecurity measures to protect consumers. An exploratory study indicates that the dangerous websites identified in the alerts are largely not detected by the safety services in web browsers. The financial security of consumers can be improved through coordination between the financial regulators, browser developers and cybersecurity services. We provide recommendations for improving the effectiveness of investor alerts through better data publication practices.

text: Investigating the data in investor alerting portals Financial regulators in many jurisdictions publish investor alerts that identify newly identified threats such as imposter websites and unlicensed firms. Many of these lists are aggregated by the International Organization of Securities Commissions. We analyse these alerts to understand if they can be used to create cybersecurity measures to protect consumers. An exploratory study indicates that the dangerous websites identified in the alerts are largely not detected by the safety services in web browsers. The financial security of consumers can be improved through coordination between the financial regulators, browser developers and cybersecurity services. We provide recommendations for improving the effectiveness of investor alerts through better data publication practices.

year: 2025-12-09

title: Correlation-based feature selection for machine learning

authors: Hall, Mark A.

abstract: A central problem in machine learning is identifying a representative set of features from which to construct a classification model for a particular task. This thesis addresses the problem of feature selection for machine learning through a correlation based approach. The central hypothesis is that good feature sets contain features that are highly correlated with the class, yet uncorrelated with each other. A feature evaluation formula, based on ideas from test theory, provides an operational definition of this hypothesis. CFS (Correlation based Feature Selection) is an algorithm that couples this evaluation formula with an appropriate correlation measure and a heuristic search strategy. 

CFS was evaluated by experiments on artificial and natural datasets. Three machine learning algorithms were used: C4.5 (a decision tree learner), IB 1 (an instance based learner), and naive Bayes. Experiments on artificial datasets showed that CFS quickly identifies and screens irrelevant, redundant, and noisy features, and identifies relevant features as long as their relevance does not strongly depend on other features. On natural domains, CFS typically eliminated well over half the features. In most cases, classification accuracy using the reduced feature set equaled or bettered accuracy using the complete feature set. Feature selection degraded machine learning performance in cases where some features were eliminated which were highly predictive of very small areas of the instance space. 

Further experiments compared CFS with a wrapper - a well known approach to feature selection that employs the target learning algorithm to evaluate feature sets. In many cases CFS gave comparable results to the wrapper, and in general, outperformed the wrapper on small datasets. CFS executes many times faster than the wrapper, which allows it to scale to larger datasets.

Two methods of extending CFS to handle feature interaction are presented and experimentally evaluated. The first considers pairs of features and the second incorporates feature weights calculated by the RELIEF algorithm. Experiments on artificial domains showed that both methods were able to identify interacting features. On natural domains, the pairwise method gave more reliable results than using weights provided by RELIEF.

text: Correlation-based feature selection for machine learning A central problem in machine learning is identifying a representative set of features from which to construct a classification model for a particular task. This thesis addresses the problem of feature selection for machine learning through a correlation based approach. The central hypothesis is that good feature sets contain features that are highly correlated with the class, yet uncorrelated with each other. A feature evaluation formula, based on ideas from test theory, provides an operational definition of this hypothesis. CFS (Correlation based Feature Selection) is an algorithm that couples this evaluation formula with an appropriate correlation measure and a heuristic search strategy. 

CFS was evaluated by experiments on artificial and natural datasets. Three machine learning algorithms were used: C4.5 (a decision tree learner), IB 1 (an instance based learner), and naive Bayes. Experiments on artificial datasets showed that CFS quickly identifies and screens irrelevant, redundant, and noisy features, and identifies relevant features as long as their relevance does not strongly depend on other features. On natural domains, CFS typically eliminated well over half the features. In most cases, classification accuracy using the reduced feature set equaled or bettered accuracy using the complete feature set. Feature selection degraded machine learning performance in cases where some features were eliminated which were highly predictive of very small areas of the instance space. 

Further experiments compared CFS with a wrapper - a well known approach to feature selection that employs the target learning algorithm to evaluate feature sets. In many cases CFS gave comparable results to the wrapper, and in general, outperformed the wrapper on small datasets. CFS executes many times faster than the wrapper, which allows it to scale to larger datasets.

Two methods of extending CFS to handle feature interaction are presented and experimentally evaluated. The first considers pairs of features and the second incorporates feature weights calculated by the RELIEF algorithm. Experiments on artificial domains showed that both methods were able to identify interacting features. On natural domains, the pairwise method gave more reliable results than using weights provided by RELIEF.

year: 1999

title: What are Bicultural Psychological Services?

authors: Thomas, David R.

subjects: Maori students

abstract: The present paper is intended to stimulate discussion on the development of bicultural psychological services in Aotearoa/New Zealand, focusing primarily on services provided by psychologists trained in tertiary educational institutions. Patterns of biculturalism emerging among human services in New Zealand are briefly described, then topics relevant to the provision of bicultural psychological services, that are appropriate for both Maori and Pakeha, in Aotearoa are discussed.

text: What are Bicultural Psychological Services? The present paper is intended to stimulate discussion on the development of bicultural psychological services in Aotearoa/New Zealand, focusing primarily on services provided by psychologists trained in tertiary educational institutions. Patterns of biculturalism emerging among human services in New Zealand are briefly described, then topics relevant to the provision of bicultural psychological services, that are appropriate for both Maori and Pakeha, in Aotearoa are discussed.

year: 1993