# Training Performance Indications for Amateur Athletes Based on Nutrition and Activity Lifelogs
## 2 Methodology
### Feature Selection  
Feature selection is one of the most critical stages in machine learning. It aims to reduce the data's dimensionality and enhance any proposed framework's performance [22]. Many feature selection works have been reported from various applications and domains. These reports converge into one common sense: feature selection suffers from many factors, such as high dimensionality, computational and storage complexity, noisy or ambiguous nature, and high performance, to name a few. Despite the challenges mentioned, feature selections bring undeniable benefits to enhance machine learning models' productivity (e.g., accuracy, complexity).  
This section discusses our feature selection mechanism, through which significant characteristics of a group and person are distributed into different discriminative sets. Our aim is to find a group of features that can leverage the common sense of one group's members while maintaining the uniqueness of each member of that group. This could contribute to developing a supervised model that could start with a few samples shared by a group of people to generate a primary instance and end with an adaptive instance when considering personal characteristics.  
Our hypothesis for feature selections is based on the following observations and facts.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Features** & **Low Level** & **Normal Level** & **High Level** \\ \hline Calories\_all\_day & 0-2000 & 2000–3000 & 3000–6000 \\ \hline exercise\_calories & 0-200 & 200–450 & 450–6000 \\ \hline distance & 0-7000 & 7000–9000 & 9000–25,000 \\ \hline exercise\_distance & 0-4000 & 4000–7000 & 7000–25,000 \\ \hline lightly\_active\_minutes & 0–1800 & 1800–2100 & 2100–42,000 \\ \hline exercise\_lightly\_minutes & 0–1800 & 1800–2100 & 2100–42,000 \\ \hline steps & 0–10,000 & 10,000–18,000 & 18,000–30,000 \\ \hline exercise\_steps & 0–2000 & 2000–4000 & 4000–30,000 \\ \hline moderately\_active\_minutes & 0–6300 & 6300–6900 & 9000 \\ \hline exercise\_moderately\_minutes & 0–6300 & 6300–6900 & 9000 \\ \hline sedentary\_minutes & 0–600 & 600–1800 & 1800–90,000 \\ \hline exercise\_sedentary\_minutes & 0–600 & 600–1800 & 1800–90,000 \\ \hline very\_active\_minutes & 0–15,000 & 15,000–15,600 & 15,600–120,000 \\ \hline exercise\_very\_minutes & 0–15,000 & 15,000–15,600 & 15,600–120,000 \\ \hline exercise\_averageHeartRate & 0–95 & 95–170 & 170–2000 \\ \hline exercise\_speed & 0–7 & 7–9 & 9–15 \\ \hline exercise\_duration & 0–1500 & 1500–2100 & 2100–9000 \\ \hline time\_per\_kilometer & 0–1500 & 1500–2100 & 2100–9000 \\ \hline exercise\_elevationGain & 0–15 & 15–100 & 100–300 \\ \hline exercise\_heartRateZones\_FatBurn\_minutes & 0–3600 & 3600–5100 & 5100–120,000 \\ \hline exercise\_heartRateZones\_Peak\_minutes & 0–11,220 & 11,220–12,000 & 12,000–30,000 \\ \hline exercise\_heartRateZones\_Cardio\_minutes & 0–6180 & 6180–7500 & 7500–30,000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data Level Determination.  
* People share common characteristics with their group and have personal characteristics that make them unique from a group.
* With the same exercise and nutrition plan, finding two people with the same outcome is problematic.
* People who prefer a self-training plan tend not to follow the plan strictly due to both subjective (e.g., tired, not in the mood) and objective reasons (e.g., busy working, unexpected meeting)  
Based on the discussions above, we created three personal, intersection, and general features, where the first describes the uniqueness of one person, the second indicates the common concrete characteristics shared inside one group, and the third is involved in characteristics that two people at least share in a group.  
It should be noted that we rely heavily on the periodic-frequent pattern mining results to build these three feature types. In other words, we consider people's habits and frequency of data factors, both for individuals and groups.