A new approach of top-down induction of decision trees for knowledge discovery

Top-down induction of decision trees is the most popular technique for classification in the field of data mining and knowledge discovery. Quinlan developed the basic induction algorithm of decision trees, ID3 (1984), and extended to C4.5 (1993). There is a lot of research work for dealing with a single attribute decision-making node (so-called the first-order decision) of decision trees. Murphy and Pazzani (1991) addressed about multiple-attribute conditions at decision-making nodes. They show that higher order decision-making generates smaller decision trees and better accuracy. However, there always exist NP-complete combinations of multiple-attribute decision-makings.;We develop a new algorithm of second-order decision-tree inductions (SODI) for nominal attributes. The induction rules of first-order decision trees are combined by \u27AND\u27 logic only, but those of SODI consist of \u27AND\u27, \u27OR\u27, and \u27OTHERWISE\u27 logics. It generates more accurate results and smaller decision trees than any first-order decision tree inductions.;Quinlan used information gains via VC-dimension (Vapnik-Chevonenkis; Vapnik, 1995) for clustering the experimental values for each numerical attribute. However, many researchers have discovered the weakness of the use of VC-dim analysis. Bennett (1997) sophistically applies support vector machines (SVM) to decision tree induction. We suggest a heuristic algorithm (SVMM; SVM for Multi-category) that combines a TDIDT scheme with SVM. In this thesis it will be also addressed how to solve multiclass classification problems.;Our final goal for this thesis is IDSS (Induction of Decision Trees using SODI and SVMM). We will address how to combine SODI and SVMM for the construction of top-down induction of decision trees in order to minimize the generalized penalty cost

Functional magnetic resonance imaging multivoxel pattern analysis reveals neuronal substrates for collaboration and competition with myopic and predictive strategic reasoning

Competition and collaboration are strategies that can be used to optimize the outcomes of social interactions. Research into the neuronal substrates underlying these aspects of social behavior has been limited due to the difficulty in distinguishing complex activation via univariate analysis. Therefore, we employed multivoxel pattern analysis of functional magnetic resonance imaging to reveal the neuronal activations underlying competitive and collaborative processes when the collaborator/opponent used myopic/predictive reasoning. Twenty- four healthy subjects participated in 2- Ã - 2 matrix- based sequential- move games. Searchlight- based multivoxel patterns were used as input for a support vector machine using nested cross- validation to distinguish game conditions, and identified voxels were validated via the regression of the behavioral data with bootstrapping. The left anterior insula (accuracy = 78.5%) was associated with competition, and middle frontal gyrus (75.1%) was associated with predictive reasoning. The inferior/superior parietal lobules (84.8%) and middle frontal gyrus (84.7%) were associated with competition, particularly in trials with a predictive opponent. The visual/motor areas were related to response time as a proxy for visual attention and task difficulty. Our results suggest that multivoxel patterns better represent the neuronal substrates underlying the social cognition of collaboration and competition intermixed with myopic and predictive reasoning than do univariate features.We employed multivoxel pattern analysis of functional magnetic resonance imaging to reveal the neuronal activations underlying competitive and collaborative processes when the collaborator/opponent used myopic/predictive reasoning in 2- Ã - 2 matrix- based sequential- move games. Searchlight- based multivoxel patterns and support vector machine were used in a nested cross- validation to distinguish game conditions, and identified voxels in the left anterior insula, middle frontal gyrus, and inferior/superior parietal lobules were validated via the regression of the behavioral data with bootstrapping by excluding potential visual attention component. Our results suggest that multivoxel patterns better represent the neuronal substrates underlying the social cognition of collaboration and competition intermixed with myopic and predictive reasoning than do univariate features.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/162700/3/hbm25127-sup-0001-Supinfo.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162700/2/hbm25127_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162700/1/hbm25127.pd

Report of the third Asian Prostate Cancer study meeting.

The Asian Prostate Cancer (A-CaP) study is an Asia-wide initiative that was launched in December 2015 in Tokyo, Japan, with the objective of surveying information about patients who have received a histopathological diagnosis of prostate cancer (PCa) and are undergoing treatment and clarifying distribution of staging, the actual status of treatment choices, and treatment outcomes. The study aims to clarify the clinical situation for PCa in Asia and use the outcomes for the purposes of international comparison. Following the first meeting in Tokyo in December 2015, the second A-CaP meeting was held in Seoul, Korea, in September 2016. This, the third A-CaP meeting, was held on October 14, 2017, in Chiang Mai, Thailand, with the participation of members and collaborators from 12 countries and regions. In the meeting, participating countries and regions presented the current status of data collection, and the A-CaP office presented a preliminary analysis of the registered cases received from each country and region. Participants discussed ongoing challenges relating to data input and collection, institutional, and legislative issues that may present barriers to data sharing, and the outlook for further patient registrations through to the end of the registration period in December 2018. In addition to A-CaP-specific discussions, a series of special lectures were also delivered on the situation for health insurance in the United States, the correlation between insurance coverage and PCa outcomes, and the outlook for robotic surgery in the Asia-Pacific region. Members also confirmed the principles of authorship in collaborative studies, with a view to publishing original articles based on A-CaP data in the future

Relationship between age and injury severity in traffic accidents involving elderly pedestrians

Objective This study aimed to examine whether injury severity differs with respect to age among elderly pedestrians involved in traffic accidents and identify factors affecting injury severity. Methods Using emergency department-based injury in-depth surveillance data, we analyzed the data of patients aged ≥60 years who were victims of pedestrian traffic accidents during 2011 to 2016. The pedestrians’ ages were divided into 5-year age strata beginning at 60 years. In a multivariate analysis, injury severity was classified as severe to critical or mild to moderate. Results The analysis included 10,449 patients. All age groups had a female predominance, and accidents most frequently occurred during the early morning. Multivariate analyses revealed that compared to the 60 to 64 years group, the odds ratios for incurring a severe injury were 1.18 (95% confidence interval [CI], 1.02 to 1.37) for the 65 to 69 years group, 1.42 (95% CI, 1.23 to 1.64) for the 70 to 74 years group, 1.70 (95% CI, 1.45 to 1.98) for the 75 to 79 years group, and 1.83 (95% CI, 1.56 to 2.15) for the ≥80 years group. Conclusion In this study of emergency department-based data, we found that injury severity increased with age among elderly victims of traffic accidents. Furthermore, injury severity varied with respect to sex, time and location of the accident, and type of vehicle involved. Therefore, measures intended to reduce and prevent traffic accidents involving elderly pedestrians should consider these findings

Asymptomatic Middle East Respiratory Syndrome coronavirus infection using a serologic survey in Korea

OBJECTIVES The rates of asymptomatic infection with Middle East Respiratory Syndrome (MERS) coronavirus vary. A serologic study was conducted to determine the asymptomatic MERS infection rate in healthcare workers and non-healthcare workers by exposure status. METHODS Study participants were selected from contacts of MERS patients based on a priority system in 4 regions strongly affected by the 2015 MERS outbreak. A sero-epidemiological survey was performed in 1,610 contacts (average duration from exposure to test, 4.8 months), and the collected sera were tested using an enzyme-linked immunespecific assay (ELISA), immunofluorescence assay (IFA), and plaque reduction neutralization antibody test (PRNT). Among the 1,610 contacts, there were 7 ELISA-positive cases, of which 1 exhibited positive IFA and PRNT results. RESULTS The asymptomatic infection rate was 0.060% (95% confidence interval, 0.002 to 0.346). The asymptomatic MERS case was a patient who had been hospitalized with patient zero on the same floor of the hospital at the same time. The case was quarantined at home for 2 weeks after discharge, and had underlying diseases, including hypertension, angina, and degenerative arthritis. CONCLUSIONS The asymptomatic infection was acquired via healthcare-associated transmission. Thus, it is necessary to extend serologic studies to include inpatient contacts who have no symptoms