I'd like to take multivariate data (independent feature1, feature2, ..., featureN) and convert each row into an image for classification. There are perhaps 10 features as well as a dependent variable, while the dependent variable is kept out of the image. A post elsewhere online suggested using recurrence plots to convert the row into an image. I tried doing this with the data (see attachment) using for example this Python library (https://github.com/laszukdawid/recurrence-plot).

A sample row of the dataset looks like this, where the exceedance column holds the dependent variable:

Exceedance is a binary variable (0 or 1). My plan is to use existing image classification approaches to classify the images. Does the use of recurrence plots in this case appear valid? Is this approach too far out? Perhaps another multivariate-to-image approach is warranted?

I'd like to take multivariate data (independent feature1, feature2, ..., featureN) and convert each row into an image for classification. There are perhaps 10 features as well as a dependent variable, while the dependent variable is kept out of the image. A post elsewhere online suggested using recurrence plots to convert the row into an image. I tried doing this with the data (see attachment) using for example this Python library (https://github.com/laszukdawid/recurrence-plot).

A sample row of the dataset looks like this, where the exceedance column holds the dependent variable:

Exceedance is a binary variable (0 or 1). My plan is to use existing image classification approaches to classify the images. Does the use of recurrence plots in this case appear valid? Is this approach too far out? Perhaps another multivariate-to-image approach is warranted?

Yes, should work. In general, multivariate time series can be used to construct a phase space trajectory (each variable would correspond to one dimension). This is a common approach since many years. In you case, you would use this as a tool for ML based image classification where the image is simply the RP. There are already many publications on this topic.