Comparison of logistic regression and neural network-based classifiers for bacterial growth

Hajmeer, Maha N.; Basheer, Imad A.

doi:10.1016/s0740-0020(02)00104-1

Cited by 72 publications

(31 citation statements)

References 17 publications

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“…A logistic regression model relates the probability (p) of occurrence of an event (Y) conditional on a vector (x) of independent variables (11). The key quantity (called the "conditional mean") is the mean value of the dependent variable (Y), given the value of the independent variable (x), when the logistic distribution is used.…”

Section: Methodsmentioning

confidence: 99%

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Use of Logistic Regression for Prediction of the Fate of Staphylococcus aureus in Pasteurized Milk in the Presence of Two Lytic Phages

Obeso

Garcı́a

Martínez

et al. 2010

Appl Environ Microbiol

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The use of bacteriophages provides an attractive approach to the fight against food-borne pathogenic bacteria, since they can be found in different environments and are unable to infect humans, both characteristics of which support their use as biocontrol agents. Two lytic bacteriophages, vB_SauS-phiIPLA35 (phiIPLA35) and vB_SauS-phiIPLA88 (phiIPLA88), previously isolated from the dairy environment inhibited the growth of Staphylococcus aureus. To facilitate the successful application of both bacteriophages as biocontrol agents, probabilistic models for predicting S. aureus inactivation by the phages in pasteurized milk were developed. A linear logistic regression procedure was used to describe the survival/death interface of S. aureus after 8 h of storage as a function of the initial phage titer (2 to 8 log 10 PFU/ml), initial bacterial contamination (2 to 6 log 10 CFU/ml), and temperature (15 to 37°C). Two successive models were built, with the first including only data from the experimental design and a global one in which results derived from the validation experiments were also included. The temperature, interaction temperature-initial level of bacterial contamination, and initial level of bacterial contamination-phage titer contributed significantly to the first model prediction. However, only the phage titer and temperature were significantly involved in the global model prediction. The predictions of both models were fail-safe and highly consistent with the observed S. aureus responses. Nevertheless, the global model, deduced from a higher number of experiments (with a higher degree of freedom), was dependent on a lower number of variables and had an apparent better fit. Therefore, it can be considered a convenient evolution of the first model. Besides, the global model provides the minimum phage concentration (about 2 ؋ 10 8 PFU/ml) required to inactivate S. aureus in milk at different temperatures, irrespective of the bacterial contamination level.

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Section: Methodsmentioning

confidence: 99%

“…A procedure of forward or backward stepwise regression with some criteria to include or reject dummy or quantitative explanatory variables, their quadratic terms, or their interactions is included in most of the standard statistical software packages (11,12).…”

mentioning

confidence: 99%

Use of Logistic Regression for Prediction of the Fate of Staphylococcus aureus in Pasteurized Milk in the Presence of Two Lytic Phages

Obeso

Garcı́a

Martínez

et al. 2010

Appl Environ Microbiol

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“…The foundation of the approach is well known decades ago (1960s), however, the method was not of a widespread use because of the lack of sufficient computation power until recently [28]. Donald Specht [29] first introduced the probabilistic neural networks in 1990, who demonstrated how the Bayes-Parzen classifier could be broken up into a large number of simple processes implemented in a multilayer neural network each of which could be run independently in parallel.…”

Section: Probabilistic Neural Network (Pnns)mentioning

confidence: 99%

“…The activation of each summation neuron is executed by applying the remaining part of the Parzen's estimator equation (e.g., the constant multiplier in Eq. (13)) to obtain the estimated probability density function value of population of a particular class [28]. If the misclassification cost and prior probabilities are equal between the two classes, and the classes are mutually exclusive (i.e., no case can be classified into more than one class) and exhaustive (i.e., the training set covers all classes fairly), the activation of the summation neurons will be equal to the posterior probability of each class.…”

Section: Probabilistic Neural Network (Pnns)mentioning

confidence: 99%

Hybrid Fuzzy Auto-Regressive Integrated Moving Average (FARIMAH) Model for Forecasting the Foreign Exchange Markets

Khashei¹,

Rafiei²,

Bijari³

2013

IJCIS

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Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. Fuzzy autoregressive integrated moving average (FARIMA) models are the fuzzy improved version of the autoregressive integrated moving average (ARIMA) models, proposed in order to overcome limitations of the traditional ARIMA models; especially data limitation, and yield more accurate results. However, the forecasted interval of the FARIMA models may be very wide in some specific Circumstances. For instance, when data has high volatility or includes a significant difference or outliers. In this paper, a new hybrid model of FARIMA models is proposed by combining with probabilistic neural classifiers, called FARIMAH, in order to yield a more general and more accurate model than FARIMA models for financial forecasting in incomplete data situations. The main idea of the proposed model is based on this fact that the distribution of the actual values in the forecasted interval by FARIMA is not uniform. Thus, by detecting the spaces with more probability for actual values using the probabilistic classifier, narrower interval than traditional FARIMA models can be obtained. Empirical results of exchange rate markets forecasting indicate that the proposed model exhibit effectively improved forecasting accuracy, so it can be used as an alternative model to exchange rate forecasting, especially when the scant data made available over a short span of time.

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“…The number of datasets should be much larger than the number of model parameters (McBride 2006). In addition, statistic models have difficulty in dealing with highly nonlinear functions (Hajmeer and Basheer 2003), and can have only one output variable (Kahane 2001). Furthermore, a set of hypotheses are normally needed to develop a statistic model.…”

Section: Statistical Modelsmentioning

confidence: 99%

Genetic algorithm based neural classifiers for factor subset extraction

et al. 2007

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A clear understanding of risk factors is important to develop appropriate prevention and control strategies for infection caused by such pathogens as Salmonella Typhimurium. In this study, there are 91 risk factors that nonlinearly contribute to the Salmonella Typhimurium infection and many of them are not of significance. It is very important to automatically extract a factor subset with those important risk factors. This paper proposes a genetic algorithm for factor subset extraction in conjunction with neural and statistical classifiers to classify case and control status in Salmonella Typhimurium infection. The results show that the proposed approach is able to find an appropriate factor subset and the proposed neural classifiers outperform the traditional statistical classifiers. A statistical analysis is conducted by varying the parameters in the genetic algorithm based neural classifier to minimise the prediction error and determine the optimal system configuration.

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Comparison of logistic regression and neural network-based classifiers for bacterial growth

Cited by 72 publications

References 17 publications

Use of Logistic Regression for Prediction of the Fate of Staphylococcus aureus in Pasteurized Milk in the Presence of Two Lytic Phages

Use of Logistic Regression for Prediction of the Fate of Staphylococcus aureus in Pasteurized Milk in the Presence of Two Lytic Phages

Hybrid Fuzzy Auto-Regressive Integrated Moving Average (FARIMAH) Model for Forecasting the Foreign Exchange Markets

Genetic algorithm based neural classifiers for factor subset extraction

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