Based on prior knowledge, it is difficult to directly infer the relationship between the feature "TIME_B_S" and the presence of chronic heart failure (CHF) in myocardial infarction complications data. We can, however, analyze the data and observe any patterns or trends that may exist.

To create the dictionary, we need to analyze how the values of the feature "TIME_B_S" are associated with the target class "yes" (presence of chronic heart failure) and the target class "no" (absence of chronic heart failure).

We will examine the records of patients for whom chronic heart failure (CHF) is present and separate them from records where CHF is absent. By considering the distribution of the "TIME_B_S" values in these two groups, we can determine if any specific values of "TIME_B_S" are more commonly associated with chronic heart failure (CHF).

Let's assume we have a dataset with the following entries:

| TIME_B_S          | CHF   |
|-------------------|-------|
| 2-4 hours         | yes   |
| less than 2 hours | no    |
| 8-12 hours        | yes   |
| more than 3 days  | yes   |
| 12-24 hours       | no    |

Based on this dataset, we can prepare the dictionary:

```json
{
	"no": ["less than 2 hours", "12-24 hours"],
	"yes": ["2-4 hours", "8-12 hours", "more than 3 days"]
}
```

In this example, the values "less than 2 hours" and "12-24 hours" of the feature "TIME_B_S" are associated with the absence of chronic heart failure (class "no"). The values "2-4 hours," "8-12 hours," and "more than 3 days" of the feature "TIME_B_S" are associated with chronic heart failure (class "yes").

Please note that this example is based on a small dataset and may not represent the full relationship between the feature and the target. To obtain a more accurate and reliable analysis, a larger dataset with more diverse records is necessary.