## 1 Introduction  
Lifelog is a Personal Health Record (PHR). PHRs are continuously generated in daily life, unlike medical records such as Electronic Medical Record (EMR) [1]. Lifelogs can be used for personal health management. Specifically, lifelog data can be utilized to check and monitor the user's health status [2, 3]. The utilization and value of lifelogs in macro-digital healthcare continues to grow [4]. This is especially the case with the utilization of weight-related lifelogs, which are strongly associated with many diseases [5]. For example, weight gain in adults is associated with an increased risk of type 2 diabetes, coronary heart disease (CHD), hypertension, cholelithiasis and various cancers [6, 7, 8, 9, 10, 11]. Therefore, body weight is an important factor in various diseases and can play an important role in analyzing various health and diseases. Recently, with the development of wearable device technology, lifelogs have become more common [12, 13], and research on related analysis methods [14, 15, 16] is being conducted. Personalized analytics and services [17] are provided by combining lifelogs with machine learning technologies. The increased need to continuously record and monitor lifelogs [18] has meant that continuous data collection and management are required. However, since abnormal data may occur due to device errors, application errors, or user errors [19], the ability to detect and process corresponding outliers is critical. An outlier is here defined as data containing features that deviate greatly from the normal value (for example, 300 cm recorded in height-related data, or sharply dropping data points in steadily increasing time series data). That is, an outlier generally refers to a value with an abnormally large deviation from the normal value [20]. However, unlike other lifelog data, weight data has low daily variability for reasons such as homeostasis [21]. Therefore, a datum can be an anomaly, even if the difference between an anomaly and a normal value is small. For these reasons, the previous outlier preprocessing methods may not fit the body weight data, so new outlier data preprocessing is required for the weight data. There are several previous studies that have inclded preprocessing data: SMOTE, SMOTE + Edited Nearest Neighbors (SMOTE + ENN), SMOTE + TomekLinks (SMOTE + Tomek), CTGAN. SMOTE is a sampling technique [22] which uses the KNN algorithm to find the data closest to the minority class data, rather than simply replicating the data, and then synthesizes the data using an interpolation method that reflects the data distribution to solve the data imbalance problem. The SMOTE + Edited Nearest Neighbors method is an ensemble method [23] that combines the over-sampling method SMOTE and the under-sampling method ENN. ENN is the under-sampling method proposed by [PERSON] (1972). When two or more of the data to which KNN (generally \(k=3\)) is applied based on the majority class are classified as minority class, the data belonging to the corresponding majority class are sampled for deletion [24]. If there are very few data belonging to the minority class, considerable data belonging to the majority class can be deleted [25]. The SMOTE + TOMEK is an ensemble technique that combines oversampling SMOTE and under-sampling TOMEK [26]. TomekLinks is an under-sampling method developed by [PERSON] (1976). Specifically, it is a sampling method that removes data belonging to multiple classes from the data for which Tomek links are established for all data [27]. Tomek link [28] is defined by Equation (1) as follows. \[x_{i}\in Normal\ class,\ x_{j}\in Outlier\ class,\
exists x_{k}\ such\ that\left(x_{k},\ x_{j}\right)<d\left(x_{i},\ x_{j}\right)\ or\ d\left(x_{k},\ x_{i}\right)<d\left(x_{i},\ x_{j}\right) \tag{1}\]  
This method can perform better than the simple SMOTE method when the imbalance ratio is large [26; 29].