Programming-by-example for data transformation to improve machine learning performance

Narita, Minori; Igarashi, Takeo

doi:10.1145/3308557.3308683

Cited by 3 publications

(2 citation statements)

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“…A few previous researchers attempted to transform their dataset into various forms to improve the performance of the machine-learning system [ 19 , 20 , 21 , 22 ]. In this research, we chose to use PM 2.5 , temperature and relative humidity data as input data of our predictive model.…”

Section: Methodsmentioning

confidence: 99%

“…For this purpose, we used machine-learning techniques and personal exposure levels of PM 2.5 , temperature, and relative humidity to identify pre-defined activity patterns. The idea was based on transforming the training data into its statistical values, which is a technique used to diversify features of data to improve the performance of classifiers [ 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Activity Pattern Classification Using Personal PM2.5 Exposure Information

Park

Kim

2020

IJERPH

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The activity pattern is a significant factor in identifying hotspots of personal exposure to air pollutants, such as PM2.5. However, the recording process of an activity pattern can be annoying to study participants, because they are often asked to bring a diary or a tracking recorder to write or validate their activity patterns when they change their activity profiles. Furthermore, the accuracy of the records of activity patterns can be lower, because people can mistakenly record them. Thus, this paper proposes an idea to overcome these problems and make the whole data-collection process easier and more reliable. Our idea was based on transforming training data using the statistical properties of the children’s personal exposure level to PM2.5, temperature, and relative humidity and applying the properties to a decision tree algorithm for classification of activity patterns. From our final machine-learning modeling processes, we observed that the accuracy for activity-pattern classification was more than 90% in both the training and test data. We believe that our methodology can be used effectively in data-collection tasks and alleviate the annoyance that study participants may feel.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Activity Pattern Classification Using Personal PM2.5 Exposure Information

Park

Kim

2020

IJERPH

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Literature Search and Review

2020

Engineering Research

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Machine Learning Interpretability to Detect Fake Accounts in Instagram

Sallah

Alaoui

Agoujil

et al. 2022

International Journal of Information Security and Privacy

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This study is related to the detection of fake accounts on Instagram dataset that used by previous works. For this purpose, various Machine Learning algorithms have been used such as Bagging and Boosting to detect fake accounts on Instagram. Machine Learning now allows eight to learn directly from data rather than human knowledge, with an increased level of accuracy. To balance the two classes of data, we used the SMOTE algorithm which allows to obtain the same number of individuals for each class. We also incorporated methods for interpreting complex Machine Learning Models to understand the reasons for a model decision like SHAP values and LIME, we preferred to use SHAP values because it provides a local and global explanation of the model and also the values add up to the real estimation of the model, which LIME does not provide. Results show an overall accuracy of 96% for the XGBoost and Random Forest. In what follows, an online fake detecting system has been developed to detect malicious accounts on the Instagram.

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Programming-by-example for data transformation to improve machine learning performance

Cited by 3 publications

References 3 publications

Machine Learning-Based Activity Pattern Classification Using Personal PM2.5 Exposure Information

Machine Learning-Based Activity Pattern Classification Using Personal PM2.5 Exposure Information

Literature Search and Review

Machine Learning Interpretability to Detect Fake Accounts in Instagram

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