A multi-class classification device, of the mobile phone type, for an application related to the classification of a mode of transport using machine learning, characterized in that it comprises: - sensors configured to acquire, at successive moments, measurements of physical quantities specific to said application, - a microprocessor configured to: - acquire said successive measurements from the sensors to calculate predictors from physical quantities of said measurements, said predictors being indicative of a position or a mobility state, - construct continuous series (aj) of samples, each sample being a vector composed of a predetermined number of predictors, - decompose each series (aj) of samples into a set of sequences (Sk), each corresponding to a specific class among a set of predefined classes, said set of predefined classes corresponding to different modes of transport, - under-sample sequences associated with classes among said set of predefined classes by retaining, for each selected sequence, a predetermined fraction of samples from said sequence, thereby forming classes associated with balanced samples, and - construct a classifier (F) defining the classification of the mode of transport based on said balanced samples using machine learning. - sensors configured to acquire, at successive moments, measurements of physical quantities specific to said application, - a microprocessor configured to: - acquire said successive measurements from the sensors to calculate predictors from physical quantities of said measurements, said predictors being indicative of a position or a mobility state, - construct continuous series (aj) of samples, each sample being a vector composed of a predetermined number of predictors, - decompose each series (aj) of samples into a set of sequences (Sk), each corresponding to a specific class among a set of predefined classes, said set of predefined classes corresponding to different modes of transport, - under-sample sequences associated with classes among said set of predefined classes by retaining, for each selected sequence, a predetermined fraction of samples from said sequence, thereby forming classes associated with balanced samples, and - construct a classifier (F) defining the classification of the mode of transport based on said balanced samples using machine learning. - acquire said successive measurements from the sensors to calculate predictors from physical quantities of said measurements, said predictors being indicative of a position or a mobility state, - construct continuous series (aj) of samples, each sample being a vector composed of a predetermined number of predictors, - decompose each series (aj) of samples into a set of sequences (Sk), each corresponding to a specific class among a set of predefined classes, said set of predefined classes corresponding to different modes of transport, - under-sample sequences associated with classes among said set of predefined classes by retaining, for each selected sequence, a predetermined fraction of samples from said sequence, thereby forming classes associated with balanced samples, and - construct a classifier (F) defining the classification of the mode of transport based on said balanced samples using machine learning. <EOS>