Dr Gea Rahman's repositories
ADF
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 associated with previously unseen classes. ADF also is capable of identifying and handling concept drift; it, however, does not forget previously gained knowledge. Moreover, ADF is capable of handling big data if the data can be divided into batches.
TLF
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 records. While existing methods often focus on one issue and leave the other one for the further work, TLF is capable of handling both issues simultaneously. In TLF, we alleviate feature discrepancy by identifying shared label distributions that act as the pivots to bridge the domains. We handle distribution divergence by simultaneously optimizing the structural risk functional, joint distributions between domains, and the manifold consistency underlying marginal distributions. Moreover, for the manifold consistency we exploit its intrinsic properties by identifying $k$ nearest neighbors of a record, where the value of k is determined automatically in TLF. Furthermore, since negative transfer is not desired, we consider only the source records that are belonging to the source pivots during the knowledge transfer. We evaluate TLF on seven publicly available natural datasets and compare the performance of TLF against the performance of eleven state-of-the-art techniques. We also evaluate the effectiveness of TLF in some challenging situations. Our experimental results, including statistical sign test and Nemenyi test analyses, indicate a clear superiority of the proposed framework over the state-of-the-art techniques.
LFD
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 all other categorical attributes and any numerical attribute that has already been categorized.
FEMI
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 similar to the record having a missing value. While identifying a group of similar records and making a guess based on the group, it applies a fuzzy clustering approach and our novel fuzzy expectation maximization algorithm.
kDMI
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. Additionally, it uses a novel approach to automatically find the value of k for each record.
SiMI
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, missing values are then imputed. To achieve a higher quality of imputation some segments are merged together using a novel approach.
weka
Now redundant weka mirror. Visit https://github.com/Waikato/weka-trunk for the real deal