A Comparison of Seven Techniques for Choosing Subsets of Pattern Recognition Properties

Mucciardi, Anthony N.; Gose, Earl E.

doi:10.1109/t-c.1971.223398

Cited by 176 publications

(47 citation statements)

References 19 publications

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“…A set of Cox models was fitted to the data with 1 to n covariates at a time. 21,22 Covariates that assessed the same parameter on a continuous or discrete scale were not included in the same model [eg. either base-line Hb as continuous (g/L) or binary (< 10 g/dL, yes/no) but not together].…”

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confidence: 99%

A pooled analysis of overall survival in COMFORT-I and COMFORT-II, 2 randomized phase III trials of ruxolitinib for the treatment of myelofibrosis

et al. 2015

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Ruxolitinib, a potent Janus kinase 1/2 inhibitor, resulted in rapid and durable improvements in splenomegaly and disease-related symptoms in the 2 phase III COMFORT studies. In addition, ruxolitinib was associated with prolonged survival compared with placebo (COMFORT-I) and best available therapy (COMFORT-II). We present a pooled analysis of overall survival in the COMFORT studies using an intent-to-treat analysis and an analysis correcting for crossover in the control arms. Overall, 301 patients received ruxolitinib (COMFORT-I, n=155; COM-FORT-II, n=146) and 227 patients received placebo (n=154) or best available therapy (n=73). After a median three years of follow up, intent-to-treat analysis showed that patients who received ruxolitinib had prolonged survival compared with patients who received placebo or best available therapy [hazard ratio=0.65; 95% confidence interval (95%CI): 0.46-0.90; P=0.01]; the crossover-corrected hazard ratio was 0.29 (95%CI: 0.13-0.63). Both patients with intermediate-2-or high-risk disease showed prolonged survival, and patients with high-risk disease in the ruxolitinib group had survival similar to that of patients with intermediate-2-risk disease in the control group. The Kaplan-Meier estimate of overall survival at week 144 was 78% in the ruxolitinib arm, 61% in the intent-to-treat control arm, and 31% in the crossover-adjusted control arm. While larger spleen size at baseline was prognostic for shortened survival, reductions in spleen size with ruxolitinib treatment correlated with longer survival. These findings are consistent with previous reports and support that ruxolitinib offers a survival benefit for patients with myelofibrosis compared with conventional therapies. (clinicaltrials.gov identifiers: COMFORT-I, NCT00952289; NCT00934544) A pooled analysis of overall survival in COMFORT-I and COMFORT-II, 2 randomized phase III trials of ruxolitinib for the treatment of myelofibrosis MethodsCOMFORT-I and COMFORT-II are randomized phase III studies comparing ruxolitinib with placebo or BAT, respectively, in patients with International Prognostic Scoring System (IPSS) intermediate-2-or high-risk primary MF (PMF), post-PV MF (PPV-MF), or post-ET MF (PET-MF) by World Health Organization and International Working Group for Myelofibrosis Research and Treatment (IWG-MRT) criteria.15,16 COMFORT-I was conducted at 89 clinical sites in the United States, Canada, and Australia, and COMFORT-II was conducted at 56 clinical sites in 9 countries across Europe (Online Supplementary Appendix). The study designs and patients' populations have been described previously.11,13 For each patient, the starting dose of ruxolitinib was determined based on base-line platelet count (15 or 20 mg twice daily [bid]) and was individually titrated over the course of treatment (5-25 mg bid) to optimize safety and efficacy. In COMFORT-II, investigator-selected BAT included any commercially available agent (as monotherapy or in combination) or no therapy, and could be changed at any time. In each study, p...

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A pooled analysis of overall survival in COMFORT-I and COMFORT-II, 2 randomized phase III trials of ruxolitinib for the treatment of myelofibrosis

et al. 2015

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“…Exhaustive search strategy is most appropriate when the number of features is sufficiently small, since it finds the optimal feature subset. However, statistical heuristics [7][8] [9] or randomized heuristics [6][10] [11][12] are used commonly since there are too many features in most cases. Each strategy has advantages as well as disadvantages in a specific domain.…”

Section: Feature Subset Selectionmentioning

confidence: 99%

Prediction of Molecular Bioactivity for Drug Design Using a Decision Tree Algorithm

Lee

Yang

2003

Discovery Science

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Abstract. A machine learning-based approach to the prediction of molecular bioactivity in new drugs is proposed. Two important aspects are considered for the task: feature subset selection and cost-sensitive classification. These are to cope with the huge number of features and unbalanced samples in a dataset of drug candidates. We designed a pattern classifier with such capabilities based on information theory and re-sampling techniques. Experimental results demonstrate the feasibility of the proposed approach. In particular, the classification accuracy of our approach was higher than that of the winner of KDD Cup 2001 competition.

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“…Feature selection allows to disTwo different approaches for feature dimensionalcard some of the irrelevant features and better perity reduction are feature selection and feature formance may be achieved by discarding such extraction. The former is to find a set of input varifeatures (Fukunaga, 1972;Mucciardi and Gose, 1971;Steppe et al, 1996). In their paper (Jenson and Shen, 2002) presented an approach to dimensionality reduction by applying the concept of fuzzy-rough sets.…”

Section: Introductionmentioning

confidence: 99%

On fuzzy-rough sets approach to feature selection

Bhatt

Gopal

2005

Pattern Recognition Letters

191

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A Comparison of Seven Techniques for Choosing Subsets of Pattern Recognition Properties

Cited by 176 publications

References 19 publications

A pooled analysis of overall survival in COMFORT-I and COMFORT-II, 2 randomized phase III trials of ruxolitinib for the treatment of myelofibrosis

A pooled analysis of overall survival in COMFORT-I and COMFORT-II, 2 randomized phase III trials of ruxolitinib for the treatment of myelofibrosis

Prediction of Molecular Bioactivity for Drug Design Using a Decision Tree Algorithm

On fuzzy-rough sets approach to feature selection

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