Comparison of Min-Max normalization and Z-Score Normalization in the K-nearest neighbor (kNN) Algorithm to Test the Accuracy of Types of Breast Cancer

Henderi, Henderi

doi:10.47738/ijiis.v4i1.73

Cited by 127 publications

(63 citation statements)

References 10 publications

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“…The MAB approach uses the experiment findings to add more contacts to the low performance version, thus allocating less traffic to the poor performance variant [13]. Theoretically, a multi-strategy algorithm can yield higher results overall (and fewer regret), but enable data to be collected about how consumers engage with various campaign variations [14].…”

Section: Methodsmentioning

confidence: 99%

Comparing Epsilon Greedy and Thompson Sampling model for Multi-Armed Bandit algorithm on Marketing Dataset

Umami

Rahmawati

2021

J. Appl. Data. Sciences.

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Background, A/B checking is a regular measure in many marketing procedures for ecommerce companies. Through well-designed A/B research, advertisers can gain insight about when and how marketing efforts can be maximized and active promotions driven. In practical terms, standard A/B experimentation makes less money relative to more advanced machine learning methods. Purposes, in order to examine the current A/B testing state, identify some popular machine learning algorithms (multi-arm bandits) which are used to optimize A/B testing, and then explain the output in some standard marketing cases of these algorithms. Methodology, In this study, the state of A/B testing have been addressed, some typical A/B learning algorithms (Multi-Arms Bandits) like Thompson Sampling, Epsilon Greedy and UCB-1 will be implemented and compared used to optimize A/B testing are described and comparable. As a result, UCB-1 and Thompson Sampling, be an exceptional winner to optimize payouts in this situation. Because it showed more effective results, without losing experimentation and statistical variations, to maximize total payouts. Based on its accuracy and strong tolerance to noise on the results, UCB-1 is the right option for MAB for a low base conversion, a limited impact size scenario.

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Section: Methodsmentioning

confidence: 99%

Comparing Epsilon Greedy and Thompson Sampling model for Multi-Armed Bandit algorithm on Marketing Dataset

Umami

Rahmawati

2021

J. Appl. Data. Sciences.

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“…First, the collected data is performed with data processing to delete the data whose state quantity is “0” and unchanged. The Z-score [ 43 ] is used for normalization processing. The Pearson correlation coefficient is used to conduct the correlation analysis with Converter_power.…”

Section: Methodsmentioning

confidence: 99%

Fault Detection of Wind Turbine Electric Pitch System Based on IGWO-ERF

Tang

et al. 2021

Sensors

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It is difficult to optimize the fault model parameters when Extreme Random Forest is used to detect the electric pitch system fault model of the double-fed wind turbine generator set. Therefore, Extreme Random Forest which was optimized by improved grey wolf algorithm (IGWO-ERF) was proposed to solve the problems mentioned above. First, IGWO-ERF imports the Cosine model to nonlinearize the linearly changing convergence factor α to balance the global exploration and local exploitation capabilities of the algorithm. Then, in the later stage of the algorithm iteration, α wolf generates its mirror wolf based on the lens imaging learning strategy to increase the diversity of the population and prevent local optimum of the population. The electric pitch system fault detection method of the wind turbine generator set sets the generator power of the variable pitch system as the main state parameter. First, it uses the Pearson correlation coefficient method to eliminate the features with low correlation with the electric pitch system generator power. Then, the remaining features are ranked by the importance of the RF features. Finally, the top N features are selected to construct the electric pitch system fault data set. The data set is divided into a training set and a test set. The training set is used to train the proposed fault detection model, and the test set is used for testing. Compared with other parameter optimization algorithms, the proposed method has lower FNR and FPR in the electric pitch system fault detection of the wind turbine generator set.

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“…For this study, since the predicted output of VGG19 always occurs on a scale of 0 to 10, this method of normalizing the input by running statistics was replaced by simply dividing the input by 10 to normalize it [17]. Further studies testing standard filtration methods in this context may be needed, but appear to cause problems by treating invisible edge scenarios as extreme outliers when they occur after a large number of states have been observed [18]. The scale on which the input is located will affect the scale on which the weights are located, so adjustments to this part of the process will have an impact on the speed of learning and which standard delta deviation is most appropriate to facilitate learning.…”

Section: Fig 2 Reward Of Bipedalwalker-v3 Policy Over Training Sessionmentioning

confidence: 99%

Training Autonomous Vehicles in Carla model using Augmented Random Search Algorithm

Riyanto

Azis

Tarwoto

et al. 2021

J. Appl. Data. Sciences.

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Background, CARLA is an open source simulator for autonomous driving research. CARLA has been developed from scratch to support the development, training and validation of autonomous driving systems. In addition to open source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that are created for this purpose and can be used freely. Methodology, Augmented Random Search (ARS) is a method that trains a one-layer perceptron on input data by adding and subtracting a series of random noise repeatedly to the weight, recording the total rewards generated by this modification in separate episodes, then making weighted modifications to weights based on these rewards in a specified number of episodes, scaled at a predetermined learning rate. Test the ability of the Augmented Random Search (ARS) algorithm to train driverless cars on data collected from the front cameras per car. Result, in this study, a framework that can be used to train driverless car policy using ARS in Carla will be built using it as the main algorithm and will provide a more realistic representation of how humans apply these controls. Novelty, this makes ARS a very lightweight method for studying complex control tasks, and the study authors found that ARS offered at least 15x more computational efficiency than the fastest competing learning methods. More consistent results from Autonomous Vehicle Training have been demonstrated using CARLA, this research has succeeded in opening up most of the opportunities for further study on the same topic, considering how many unique situations the workers in this study experienced in the Carla simulator during the exercise.

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Comparison of Min-Max normalization and Z-Score Normalization in the K-nearest neighbor (kNN) Algorithm to Test the Accuracy of Types of Breast Cancer

Cited by 127 publications

References 10 publications

Comparing Epsilon Greedy and Thompson Sampling model for Multi-Armed Bandit algorithm on Marketing Dataset

Comparing Epsilon Greedy and Thompson Sampling model for Multi-Armed Bandit algorithm on Marketing Dataset

Fault Detection of Wind Turbine Electric Pitch System Based on IGWO-ERF

Training Autonomous Vehicles in Carla model using Augmented Random Search Algorithm

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