The occurrences of noisy data in data set can significantly impact prediction of any
meaningful information. Many empirical studies have shown that noise in data set …

Context Despite recent attention given to Software Defect Prediction (SDP), the lack of any
systematic effort to assess existing empirical evidence on the application of Deep Learning …

Label noise is an important issue in classification, with many potential negative
consequences. For example, the accuracy of predictions may decrease, whereas the …

This paper presents the process required to implement a data driven Predictive
Maintenance (PdM) not only in the machine decision making, but also in data acquisition …

Background In the last few years, multi-omics data, that is, datasets containing different types
of high-dimensional molecular variables for the same samples, have become increasingly …

Support Vector Machines (SVMs) are vulnerable to targeted training data manipulations
such as poisoning attacks and label flips. By carefully manipulating a subset of training …

In most classification problems, sometimes in order to achieve better results, data cleaning is
used as a preprocessing technique. The purpose of data cleaning is to remove noise …

Abstract Machine learning is becoming an integral part of the Design-Build-Test-Learn cycle
in biotechnology. Machine learning models learn from collected datasets such as omics data …

Detecting Intrusions and anomalies is becoming much more challenging with new attacks
pop** out over a period of time. Achieving better accuracies by applying benchmark …

Noise detection for software measurement datasets is a topic of growing interest. The
presence of class and attribute noise in software measurement datasets degrades the …