Machine Learning Literature Review

CHAPTER TWO
LITERATURE REVIEW
2.0 INTRODUCTION
Data is derived from the Latin word “Datum” and can be defined as a representation of facts, concepts or instructions in a formalized manner suitable for communication, interpretation, or processing by humans or by automatic means (Hicks 1993). Data is referred to as just mere facts unless processed to obtain information and knowledge. They can be classified into three levels of abstraction with data being the lowest level of abstraction and knowledge the highest level of abstraction.
In the last decade, the amount of data collected and generated in all industries is growing at a fast rate (Brachman et al., 1996). From the financial sector, to the manufacturing industries to the medical sector

The developments of various systems achieved with the concept of machine have been tre

2.1.1APPLICATION OF MACHINE LEARNING
According to (Shhab, et al.) the following are some applications of machine learning. Diagnosis: Defining malfunctions based on an object behavior then recommending solutions. Pattern Recognition: Identification of objects and images by their shapes, forms, outlines, color, surface, texture, temperature, or other attribute, usually by automatic means. Prediction: Predicting the behavior of an object in the future pertaining to its past one. Classification: Assigning an object to a pre-defined class. Clustering: Dividing a heterogeneous group of objects into homogeneous subgroups. Optimization: Improving the quality of solutions until an optimal one is found. Control: Governing the behavior of an object to meet specified

Rough set evolved from the fact that in classical set theory, data was grouped or classified as either a subset or a complement of the set. Rough set was created to handle those data sets that can neither stand as a subset nor a complement of the set. It is important to note that one of the significance of rough set that makes it unique and sort after is that it deals with uncertainty and decision making under circumstances with insufficient information and also it does not need any preliminary or additional information about the data. Therefore, it classifies imprecise, uncertain or incomplete information expressed in terms of data.
Rough set deals with classificatory analysis of data tables, the data can be acquired from either from measurements or records (such as financial records, stock rates, medical records or reports, students’ attendance score or report, examination scores, scientific reports, etc.) or human experts and the sole aim of rough set is to analyze and combine approximations of concepts from the acquired data. The dataset usually represented in a tabular form is known as the Information