The Experts below are selected from a list of 18 Experts worldwide ranked by ideXlab platform

Volker W Rahlfs - One of the best experts on this subject based on the ideXlab platform.

  • comments on number needed to treat derived from ordinal scales
    Statistical Methods in Medical Research, 2014
    Co-Authors: Helmuth Zimmermann, Volker W Rahlfs
    Abstract:

    A well-known effect size measure in evidence-based medicine is the number-needed-to-treat (NNT) in order to get one more responder or one more patient with improvement. It is defined as the reciprocal of the simple risk difference of the two groups being compared (test group with innovation product and control group). The NNT effect size measure has been largely accepted in the scientific community. It was thus desirable to develop the NNT for other well-known effect size measures beyond pure risk differences. It was in this Journal that Kraemer 1 advocated using the Mann–Whitney difference superiority measure P(X < Y) � P(Y < X) as a generalized risk difference and interpreted the reciprocal as NNT. Also there have been several more papers by Kraemer and coworkers recommending this type of interpretation. 2–4 Several researchers in the biostatistical field accepted this definition or re-invented it with a different rationale. Quite recently there appeared a publication, in which it was shown that the Kraemer method did not fit with the usual procedure of a responder analysis, with responders obtained by dichotomization of an ordinal or continuous scale: 5 The NNT number by the Kraemer method simply was much too small when compared with the well-known responder analysis NNT number. In the following we will show that the Derivation Rule currently being used for NNT is not correct and should be replaced by another one. Using this new Rule the implausible difference between the two NNT Derivations as presented by Furukawa and Leucht 5 will vanish. We will clarify the relations between a risk difference and the Mann–Whitney superiority measure P(X < Y) þ 0.5� P(X ¼ Y), in some publications also called area under curve (AUC) because this measure is identical with the AUC in the receiver operating graph (ROC), well-known in the field of medical diagnostics. Our arguments will become clearer when using the percentile-percentile (P-P) plot, 6 which in principle is identical with the ROC graph. The P-P plot is a graph of an empirical distribution function (EDF) of one group against that of another group. Figure 1 gives the P-P plot for a binary scale with the observed risk difference (RD) as could be shown in a two-by-two table. Note that the perpendicular line from the P-P function to the diagonal is identical with the risk difference. Figure 1 shows the area of the triangle (O, P, I )[ ¼ Area of The Triangle (ATR)] which is exactly as large as half the risk difference RD.

Helmuth Zimmermann - One of the best experts on this subject based on the ideXlab platform.

  • comments on number needed to treat derived from ordinal scales
    Statistical Methods in Medical Research, 2014
    Co-Authors: Helmuth Zimmermann, Volker W Rahlfs
    Abstract:

    A well-known effect size measure in evidence-based medicine is the number-needed-to-treat (NNT) in order to get one more responder or one more patient with improvement. It is defined as the reciprocal of the simple risk difference of the two groups being compared (test group with innovation product and control group). The NNT effect size measure has been largely accepted in the scientific community. It was thus desirable to develop the NNT for other well-known effect size measures beyond pure risk differences. It was in this Journal that Kraemer 1 advocated using the Mann–Whitney difference superiority measure P(X < Y) � P(Y < X) as a generalized risk difference and interpreted the reciprocal as NNT. Also there have been several more papers by Kraemer and coworkers recommending this type of interpretation. 2–4 Several researchers in the biostatistical field accepted this definition or re-invented it with a different rationale. Quite recently there appeared a publication, in which it was shown that the Kraemer method did not fit with the usual procedure of a responder analysis, with responders obtained by dichotomization of an ordinal or continuous scale: 5 The NNT number by the Kraemer method simply was much too small when compared with the well-known responder analysis NNT number. In the following we will show that the Derivation Rule currently being used for NNT is not correct and should be replaced by another one. Using this new Rule the implausible difference between the two NNT Derivations as presented by Furukawa and Leucht 5 will vanish. We will clarify the relations between a risk difference and the Mann–Whitney superiority measure P(X < Y) þ 0.5� P(X ¼ Y), in some publications also called area under curve (AUC) because this measure is identical with the AUC in the receiver operating graph (ROC), well-known in the field of medical diagnostics. Our arguments will become clearer when using the percentile-percentile (P-P) plot, 6 which in principle is identical with the ROC graph. The P-P plot is a graph of an empirical distribution function (EDF) of one group against that of another group. Figure 1 gives the P-P plot for a binary scale with the observed risk difference (RD) as could be shown in a two-by-two table. Note that the perpendicular line from the P-P function to the diagonal is identical with the risk difference. Figure 1 shows the area of the triangle (O, P, I )[ ¼ Area of The Triangle (ATR)] which is exactly as large as half the risk difference RD.

Wei Bin Liang - One of the best experts on this subject based on the ideXlab platform.

  • robust dialogue act detection based on partial sentence tree Derivation Rule and spectral clustering algorithm
    Eurasip Journal on Audio Speech and Music Processing, 2012
    Co-Authors: Chiaping Chen, Wei Bin Liang
    Abstract:

    A novel approach for robust dialogue act detection in a spoken dialogue system is proposed. Shallow representation named partial sentence trees are employed to represent automatic speech recognition outputs. Parsing results of partial sentences can be decomposed into Derivation Rules, which turn out to be salient features for dialogue act detection. Data-driven dialogue acts are learned via an unsupervised learning algorithm called spectral clustering, in a vector space whose axes correspond to Derivation Rules. The proposed method is evaluated in a Mandarin spoken dialogue system for tourist-information services. Combined with information obtained from the automatic speech recognition module and from a Markov model on dialogue act sequence, the proposed method achieves a detection accuracy of 85.1%, which is significantly better than the baseline performance of 62.3% using a naive Bayes classifier. Furthermore, the average number of turns per dialogue session also decreases significantly with the improved detection accuracy.

Chiaping Chen - One of the best experts on this subject based on the ideXlab platform.

  • robust dialogue act detection based on partial sentence tree Derivation Rule and spectral clustering algorithm
    Eurasip Journal on Audio Speech and Music Processing, 2012
    Co-Authors: Chiaping Chen, Wei Bin Liang
    Abstract:

    A novel approach for robust dialogue act detection in a spoken dialogue system is proposed. Shallow representation named partial sentence trees are employed to represent automatic speech recognition outputs. Parsing results of partial sentences can be decomposed into Derivation Rules, which turn out to be salient features for dialogue act detection. Data-driven dialogue acts are learned via an unsupervised learning algorithm called spectral clustering, in a vector space whose axes correspond to Derivation Rules. The proposed method is evaluated in a Mandarin spoken dialogue system for tourist-information services. Combined with information obtained from the automatic speech recognition module and from a Markov model on dialogue act sequence, the proposed method achieves a detection accuracy of 85.1%, which is significantly better than the baseline performance of 62.3% using a naive Bayes classifier. Furthermore, the average number of turns per dialogue session also decreases significantly with the improved detection accuracy.

Machado Penousal - One of the best experts on this subject based on the ideXlab platform.

  • Probabilistic Grammatical Evolution
    2021
    Co-Authors: Mégane Jessica, Lourenço Nuno, Machado Penousal
    Abstract:

    Grammatical Evolution (GE) is one of the most popular Genetic Programming (GP) variants, and it has been used with success in several problem domains. Since the original proposal, many enhancements have been proposed to GE in order to address some of its main issues and improve its performance. In this paper we propose Probabilistic Grammatical Evolution (PGE), which introduces a new genotypic representation and new mapping mechanism for GE. Specifically, we resort to a Probabilistic Context-Free Grammar (PCFG) where its probabilities are adapted during the evolutionary process, taking into account the productions chosen to construct the fittest individual. The genotype is a list of real values, where each value represents the likelihood of selecting a Derivation Rule. We evaluate the performance of PGE in two regression problems and compare it with GE and Structured Grammatical Evolution (SGE). The results show that PGE has a a better performance than GE, with statistically significant differences, and achieved similar performance when comparing with SGE