This study introduces a novel DL-based framework called ADeepWeeD for weed classification that facilitates adaptive (i.e. incremental) learning so that it can handle new weed species by keeping tra…

We present a framework called TLF that builds a classifier for the target domain having only few labeled training records by transferring knowledge from the source domain having many labeled record…

kDMI employs two levels of horizontal partitioning (based on a decision tree and k-NN algorithm) of a data set, in order to find the records that are very similar to the one with missing value/s. A…

EDI uses two layers/steps of imputation namely the Early-Imputation step and the Advanced-Imputation step.

LFD is a data-driven discretization technique that does not require any user input. LFD uses low frequency values as cut points and thus reduces the information loss due to discretization. It uses …

SiMI imputes numerical and categorical missing values by making an educated guess based on records that are similar to the record having a missing value. Using the similarity and correlations, miss…

FIMUS imputes numerical and categorical missing values by using a data set’s existing patterns including co-appearances of attribute values, correlations among the attributes and similarity of valu…

FEMI (Fuzzy Clustering-based Missing Value Imputation Framework) for data preprocessing. FEMI imputes numerical and categorical missing values by making an educated guess based on records that are …

Adaptive Decision Forest(ADF) is an incremental machine learning framework called to produce a decision forest to classify new records. ADF is capable to classify new records even if they are assoc…

CAIRAD class implements the CAIRAD techique for detecting noisy values in a dataset for Weka

DMI Class implements the DMI imputation algorithm for imputing missing values in a dataset from Rahman, M. G., and Islam, M. Z. (2013): Missing Value Imputation Using Decision Trees and Decision Fo…