Improving accuracy metric with precision and recall metrics for optimizing stochastic classifier

All stochastic classifiers attempt to improve their classification performance by constructing an optimized classifier.Typically, all of stochastic classification algorithms employ accuracy metric to discriminate an optimal solution.However, the use of accuracy metric could lead the solution towards the sub-optimal solution due less discriminating power.Moreover, the accuracy metric also unable to perform optimally when dealing with imbalanced class distribution. In this study, we propose a new evaluation metric that combines accuracy metric with the extended precision and recall metrics to negate these detrimental effects.We refer the new evaluation metric as optimized accuracy with recall-precision (OARP). This paper demonstrates that the OARP metric is more discriminating than the accuracy metric and able to perform optimally when dealing with imbalanced class distribution using one simple counter-example.We also demonstrate empirically that a naïve stochastic classification algorithm, which is Monte Carlo Sampling (MCS) algorithm trained with the OARP metric, is able to obtain better predictive results than the one trained with the accuracy and FMeasure metrics.Additionally, the t-test analysis also shows a clear advantage of the MCS model trained with the OARP metric over the two selected metrics for almost five medical data sets

OAERP : A Better Measure than Accuracy in Discriminating a Better Solution for Stochastic Classification Training

The use of accuracy metric for stochastic classification training could lead the solution selecting towards the sub-optimal solution due to its less distinctive value and also unable to perform optimally when confronted with imbalanced class problem. In this study, a new evaluation metric that combines accuracy metric with the extended precision and recall metrics to negate these detrimental effects was proposed. This new evaluation metric is known as Optimized Accuracy with Extended Recall-precision (OAERP). By using two examples, the results has shown that the OAERP metric has produced more distinctive and discriminating values as compared to accuracy metric. This paper also empirically demonstrates that Monte Carlo Sampling (MCS) algorithm that is trained by OAERP metric was able to obtain better predictive results than the one trained by the accuracy metric alone, using nine medical data sets. In addition, the OAERP metric also performed effectively when dealing with imbalanced class problems. Moreover, the t-test analysis also shows a clear advantage of the MCS model trained by the OAERP metric against its previous metric over five out of nine medical data sets. From the abovementioned results, it is clearly indicates that the OAERP metric is more likely to choose a better solution during classification training and lead towards a better trained classification model

Improving Accuracy Metric with Precision and Recall Metrics for Optimizing Stochastic Classifier

All stochastic classifiers attempt to improve their classifica-tion performance by constructing an optimized classifier. Typically, all of stochastic classification algorithms employ accuracy metric to discriminate an optimal solution. However, the use of accuracy metric could lead the so-lution towards the sub-optimal solution due less discriminating power. Moreover, the accuracy metric also unable to perform optimally when deal-ing with imbalanced class distribution. In this study, we propose a new evaluation metric that combines accuracy metric with the extended precision and recall metrics to negate these detrimental effects. We refer the new evaluation metric as optimized accuracy with recall-precision (OARP). This paper demonstrates that the OARP metric is more discriminating than the accuracy metric and able to perform optimally when dealing with imba-lanced class distribution using one simple counter-example. We also dem-onstrate empirically that a naïve stochastic classification algorithm, which is Monte Carlo Sampling (MCS) algorithm trained with the OARP metric, is able to obtain better predictive results than the one trained with the accuracy and F-Measure metrics. Additionally, the t-test analysis also shows a clear advantage of the MCS model trained with the OARP metric over the two se-lected metrics for almost five medical data sets

A Hybrid Evaluation Metric for Optimizing Classifier

The accuracy metric has been widely used for discriminating and selecting an optimal solution in constructing an optimized classifier. However, the use of accuracy metric leads the searching process to the sub-optimal solutions due to its limited capability of discriminating values. In this study, we propose a hybrid evaluation metric, which combines the accuracy metric with the precision and recall metrics. We call this new performance metric as Optimized Accuracy with Recall-Precision (OARP). This paper demonstrates that the OARP metric is more discriminating than the accuracy metric using two counter-examples. To verify this advantage, we conduct an empirical verification using a statistical discriminative analysis to prove that the OARP is statistically more discriminating than the accuracy metric. We also empirically demonstrate that a naive stochastic classification algorithm trained with the OARP metric is able to obtain better predictive results than the one trained with the conventional accuracy metric. The experiments have proved that the OARP metric is a better evaluator and optimizer in the constructing of optimized classifier

A hybrid evaluation metric for optimizing classifier

The accuracy metric has been widely used for discriminating and selecting an optimal solution in constructing an optimized classifier. However, the use of accuracy metric leads the searching process to the sub-optimal solutions due to its limited capability of discriminating values. In this study, we propose a hybrid evaluation metric, which combines the accuracy metric with the precision and recall metrics. We call this new performance metric as Optimized Accuracy with Recall-Precision (OARP). This paper demonstrates that the OARP metric is more discriminating than the accuracy metric using two counter-examples. To verify this advantage, we conduct an empirical verification using a statistical discriminative analysis to prove that the OARP is statistically more discriminating than the accuracy metric. We also empirically demonstrate that a naive stochastic classification algorithm trained with the OARP metric is able to obtain better predictive results than the one trained with the conventional accuracy metric. The experiments have proved that the OARP metric is a better evaluator and optimizer in the constructing of optimized classifier

Seasonal distribution of anti-malarial drug resistance alleles on the island of Sumba, Indonesia.

BACKGROUND: Drug resistant malaria poses an increasing public health problem in Indonesia, especially eastern Indonesia, where malaria is highly endemic. Widespread chloroquine (CQ) resistance and increasing sulphadoxine-pyrimethamine (SP) resistance prompted Indonesia to adopt artemisinin-based combination therapy (ACT) as first-line therapy in 2004. To help develop a suitable malaria control programme in the district of West Sumba, the seasonal distribution of alleles known to be associated with resistance to CQ and SP among Plasmodium falciparum isolates from the region was investigated. METHODS: Plasmodium falciparum isolates were collected during malariometric surveys in the wet and dry seasons in 2007 using two-stage cluster sampling. Analysis of pfcrt, pfmdr1, pfmdr1 gene copy number, dhfr, and dhps genes were done using protocols described previously. RESULTS AND DISCUSSION: The 76T allele of the pfcrt gene is nearing fixation in this population. Pfmdr1 mutant alleles occurred in 72.8% and 53.3%, predominantly as 1042D and 86Y alleles that are mutually exclusive. The prevalence of amplified pfmdr1 was found 41.9% and 42.8% of isolates in the wet and dry seasons, respectively. The frequency of dhfr mutant alleles was much lower, either as a single 108N mutation or paired with 59R. The 437G allele was the only mutant dhps allele detected and it was only found during dry season. CONCLUSION: The findings demonstrate a slighly higher distribution of drug-resistant alleles during the wet season and support the policy of replacing CQ with ACT in this area, but suggest that SP might still be effective either alone or in combination with other anti-malarials

The BaBar detector: Upgrades, operation and performance

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