A method for automated and simultaneous selection of a feature subset from a plurality of features and a classifier from a plurality of classifier types to find an optimal combination of feature subset-classifier pair for a classification task, the method comprising: extracting, by a processor, the plurality of features from a plurality of signals acquired from sensors attached to at least one of a subject and an object of interest, wherein the plurality of signals comprise at least one of a electrocardiography signal, a phonogram signal, a photo plethysmography signal or signals from the sensors placed on a computer numerical control machine, wherein the plurality of signals include signals from multiple signal domain representations; deriving, by the processor, a set of features from the extracted plurality of features using a plurality of statistical measures from at least one of temporal, spectral and wavelet domains corresponding to the extracted plurality of features, wherein the plurality of statistical measures comprises mean, variance, skewness and kurtosis; creating, by the processor, a plurality of ranked lists by ranking the derived set of features, wherein each ranked list of the plurality of ranked lists is created for a unique ranking objective selected from a plurality of ranking objectives, wherein the plurality of ranked lists for the plurality of ranking objectives are created using a plurality of filter-based feature ranking methods, wherein the plurality of ranked lists with corresponding derived set of features in each ranked list is represented as a feature matrix and wherein the plurality of filter-based feature ranking methods comprise a minimum Redundancy Maximum Relevance (mRMR), a Conditional Infomax Feature Extraction (CIFE), a distance correlation and a Maximal Information Coefficient; and executing, by the processor, a joint optimization technique to jointly determine the feature subset from each ranked list of the plurality of ranked lists and a classifier from the plurality of classifiers types, such that a performance objective for the classification task is satisfied, wherein the joint optimization technique is executed using a relax-greedy approach to find the optimal combination of feature subset-classifier pair under the performance objective, wherein the relax-greedy approach utilizes a search process based on a patience function γ(δ), by selecting a low performing feature if the low performing feature in combination with other features demonstrates an incremental performance gain within a patience window δ max, and wherein the performance objective is calculated for maximizing sensitivity, maximizing F 1-score and maximizing accuracy. extracting, by a processor, the plurality of features from a plurality of signals acquired from sensors attached to at least one of a subject and an object of interest, wherein the plurality of signals comprise at least one of a electrocardiography signal, a phonogram signal, a photo plethysmography signal or signals from the sensors placed on a computer numerical control machine, wherein the plurality of signals include signals from multiple signal domain representations; deriving, by the processor, a set of features from the extracted plurality of features using a plurality of statistical measures from at least one of temporal, spectral and wavelet domains corresponding to the extracted plurality of features, wherein the plurality of statistical measures comprises mean, variance, skewness and kurtosis; creating, by the processor, a plurality of ranked lists by ranking the derived set of features, wherein each ranked list of the plurality of ranked lists is created for a unique ranking objective selected from a plurality of ranking objectives, wherein the plurality of ranked lists for the plurality of ranking objectives are created using a plurality of filter-based feature ranking methods, wherein the plurality of ranked lists with corresponding derived set of features in each ranked list is represented as a feature matrix and wherein the plurality of filter-based feature ranking methods comprise a minimum Redundancy Maximum Relevance (mRMR), a Conditional Infomax Feature Extraction (CIFE), a distance correlation and a Maximal Information Coefficient; and executing, by the processor, a joint optimization technique to jointly determine the feature subset from each ranked list of the plurality of ranked lists and a classifier from the plurality of classifiers types, such that a performance objective for the classification task is satisfied, wherein the joint optimization technique is executed using a relax-greedy approach to find the optimal combination of feature subset-classifier pair under the performance objective, wherein the relax-greedy approach utilizes a search process based on a patience function γ(δ), by selecting a low performing feature if the low performing feature in combination with other features demonstrates an incremental performance gain within a patience window δ max, and wherein the performance objective is calculated for maximizing sensitivity, maximizing F 1-score and maximizing accuracy.