Utility of real-time decision-making in commercial data stream mining domains

The objective is to measure utility of real-time commercial decision making. It is important due to a higher possibility of mistakes in real-time decisions, problems with recording actual occurrences, and significant costs associated with predictions produced by algorithms. The first contribution is to use overall utility and represent individual utility with a monetary value instead of a prediction. The second is to calculate the benefit from predictions using the utility-based decision threshold. The third is to incorporate cost of predictions. For experiments, overall utility is used to evaluate communal and spike detection, and their adaptive versions. The overall utility results show that with fewer alerts, communal detection is better than spike detection. With more alerts, adaptive communal and spike detection are better than their static versions. To maximise overall utility with all algorithms, only 1% to 4% in the highest predictions should be alerts.

Subcellular localisation of proteins in fluorescent microscope images using a random forest

This paper presents a system that employs random forests to formulate a method for subcellular localisation of proteins. A random forest is an ensemble learner that grows classification trees. Each tree produces a classification decision, and an integrated output is calculated. The system classifies the protein-localisation patterns within fluorescent microscope images. 2D images of HeLa cells that include all major classes of subcellular structures, and the associated feature set are used. The performance of the developed system is compared against that of the support vector machine and decision tree approaches. Three experiments are performed to study the influence of the training and test set size on the performance of the examined methods. The calculated classification errors and execution times are presented and discussed. The lowest classification error (2.9%) has been produced by the developed system.

A random forest for lung nodule identification

A method is presented for identification of lung nodules. It includes three stages: image acquisition, background removal, and nodule detection. The first stage improves image quality. The second stage extracts long lobe regions. The third stage detects lung nodules. The method is based on the random forest learner. Training set contains nodule, non-nodule, and false-positive patterns. Test set contains randomly selected images. The developed method is compared against the support vector machine. True-positives of 100% and 85.9%, and false-positives of 1.27 and 1.33 per image were achieved by the developed method and the support vector machine, respectively.

Random walk versus multiple trend breaks in stock prices : evidence from 15 European markets

This letter extends research reported in Narayan and Smyth (2005) by employing multiple trend break unit root tests to examine the random walk hypothesis for 15 European stock market indices. The results provide strong support for the view that stock prices are characterized by a random walk.

Mean reversion versus random walk in G7 stock prices evidence from multiple trend break unit root tests

This paper provides evidence on the random walk hypothesis in G7 stock price indices using unit root tests which allow for one and two structural breaks in the trend. Of the seven countries we find, at best, evidence of mean reversion in the stock price index of Japan. Thus, overall, our results support the random walk hypothesis. We also consider the implications of the identified structural breaks for movement in stock prices over time. Our main conclusion from this exercise is that the second break in stock prices has had a detrimental effect on movements in stock prices in the G7 countries.

The bipolar depression rating scale (BDRS) : its development, validation and utility

Objectives: Unipolar and bipolar depression differ neurobiologically and in clinical presentation. Existing depression rating instruments, used in bipolar depression, fail to capture the necessary phenomenological nuances, as they are based on and skewed towards the characteristics of unipolar depression. Both clinically and in research there is a growing need for a new observer-rated scale that is specifically designed to assess bipolar depression.

Methods: An instrument reflecting the characteristics of bipolar depression was drafted by the authors, and administered to 122 participants aged 18–65 (44 males and 78 females) with a diagnosis of DSM-IV bipolar disorder, who were currently experiencing symptoms of depression. The Bipolar Depression Rating Scale (BDRS) was administered together with the Hamilton Depression Rating Scale (HAM-D), Montgomery Asberg Depression Rating Scale (MADRS) and Young Mania Rating Scale (YMRS).

Results: The BDRS has strong internal consistency (Cronbach's alpha = 0.917), and robust correlation coefficients with the MADRS (r = 0.906) and HAM-D (r = 0.744), and the mixed subscale correlated with the YMRS (r = 0.757). Exploratory factor analysis showed a three-factor solution gave the best account of the data. These factors corresponded to depression (somatic), depression (psychological) and mixed symptom clusters.

Conclusions: This study provides evidence for the validity of the BDRS for the measurement of depression in bipolar disorder. These results suggest good internal validity, provisional evidence of inter-rater reliability and strong correlations with other depression rating scales.

A method for evaluating workplace utility

Acquiring office buildings that provide the required level of utility, and maintaining the buildings in that state, should be a priority for any organisation. Failure to do so may give rise to increased churn, reduced productivity, higher employee turnover, increased staff absenteeism and rising health care costs related to heightened stress. There is, however, no single measure of office building utility. Discusses the development of a valid and reliable scale for measuring the utility of public sector office buildings. Data collection involved the use of focus groups and an online survey of 1,800 building occupants. The findings suggest that the utility of public sector office buildings can be measured using a 22-item scale comprising four dimensions. The potential applications of the scale and its use in current research are examined.

Spatial ecology of a root parasite : from pattern to process

Occurrence patterns of parasitic plants are constrained by the distribution of suitable hosts and movement patterns of seed vectors and, accordingly, represent a simplified system to study many aspects of spatial ecology and determinants of distribution. Previous work has focused on the aerially hemiparasitic mistletoes, and it is unclear whether root parasites are affected by similar factors. Here, we evaluate spatial patterns in the root parasitic Santalum lanceolatum in an arid shrubland in north-western New South Wales, central Australia. In this region, the principal host is a long-lived nitrogen fixing shrub Acacia tetragonophylla closely associated with ephemeral creek-lines. The location of 765 individuals of both species was mapped along a 250-m section of creek-line using a total survey station, and occurrence patterns of the root parasite related to host distribution and landscape context. We used Ripley's K-function and the O-ring statistic to determine whether the distribution of S. lanceolatum was random, aggregated or regular; the spatial scales at which these patterns occurred; and to quantify any spatial associations between the parasite and its host, A. tetragonophylla. While acacias were closely associated with the creek-line, S. lanceolatum plants were more tightly clustered, displaying significant clustering at two spatial scales (1.2 m and 8.8 m). We suggest that host quality may act as an important constraint, with only those acacias growing in or near the creek-line being physiologically capable of supporting a parasite to maturity. Insights gained from spatial analysis are used to guide ongoing research in this system, and highlight the utility of the O-ring statistic for understanding patterns of distribution affected by multiple processes operating at critical scales.

A comparison of the ICI Pillbox and the Random Tumble Methods is assessing pilling and appearance change of worsted spun cashmere and cashmere-wool blend knitwear

This study evaluated the differences between two international test methods on the assessment of pilling and appearance change of worsted spun cashmere and superfine wool knitwear and their blends. Differences between the standard ICI Pill Box Method and the Random Tumble Method were found in both the significance and magnitude of resistance to pilling and appearance change and the amount of fabric mass loss of worsted spun cashmere and cashmere superfine wool blend knit fabrics. The ICI Pill Box Method differentiated to a greater extent the effects of wool type and blend ratio of cashmere and wool compared with the Random Tumble Method. Generally the addition of cashmere or low crimp superfine wool resulted in fabrics being more resistance to pilling and appearance change compared with fabrics made from high crimp superfine wool. This was associated with increased fabric mass loss when assessed by the ICI Pill Box Method but not with the Random Tumble Method. KEYWORDS: Cashmere, crimp, wool, pilling, appearance change, knitwear

Testing for a random walk in Italy's inflation in the post-war period, 1947-1996

This paper investigates the unit root properties of Italy’s inflation rate in the post-war period (1947-1996). To achieve the aim of this study, the Zivot and Andrews (1992) one break test and the Lumsdaine and Papell (1997) two breaks test for unit roots are applied. It is found that inflation for Italy was a non-stationary breakpoint for the period 1947-1996. This result has important implications for econometric modeling and in understanding the behavior of shocks to Italy’s inflation.

Testing for the random walk hypothesis in the case of visitor arrivals : evidence from Indian tourism

Testing for the random walk hypothesis, which asserts that a series is a non-stationary process or a unit root process, in the case of visitor arrivals has important implications for policy. If, for instance, visitor arrivals are characterized by a unit root, then it implies that shocks to visitor arrivals are permanent. However, if visitor arrivals are without a unit root, this implies that shocks to visitor arrivals are temporary. This study provides evidence on the random walk hypothesis for visitor arrivals to India using the recently developed Im et al. (2003) and Maddala and Wu (1999) panel unit root tests. Both tests allow one to reject the random walk hypothesis, implying that shocks to visitor arrivals to India from the 10 major source markets have a temporary effect on visitor arrivals.

Are OECD stock prices characterized by a random walk? Evidence from sequential trend break and panel data models

This paper examines whether stock prices for a sample of 22 OECD countries can be best represented as mean reversion or random walk processes. A sequential trend break test proposed by Zivot and Andrews is implemented, which has the advantage that it can take account of a structural break in the series, as well as panel data unit root tests proposed by Im et al., which exploits the extra power in the panel properties of the data. Results provide strong support for the random walk hypothesis.