Decision Trees: C4.5 Classifier Advantages Classifiers produced by C4.5 are either expressed as decision trees or rulesets. Our focus in this discussion is on decision trees and here we look at their advantages. C4.5 classifier is based on the ID3 2 algorithm whose primary aim is to find small decision trees. Based on this we can say that the decision trees produced are small and simple to understand (Witten, Frank, 2000). Another advantage of this classifier is that the source code is readily available. Thirdly, unavailable attribute values are accounted for in C4.5 by assessing the gain using the records where the particular attribute is defined. A fourth advantage is that; it can handle both continuous and discrete attributes. Attributes with a continuous range, we create partitions based on a predetermined pattern in the training set and calculate the gain on each partition, (Quinlan, 1993). Then the partition that maximizes the gain is picked. Lastly, tree pruning after creation ensures tree simplicity by replacing some branches with leaf nodes. Disadvantages C4.5 classifiers are basically slower in terms of processing speed. For instance, a task that will take C4.5 15hours to complete; C5.0 will take only 2.5 minutes. Secondly, it is inefficient in memory usage meaning that some tasks will not complete on 32-bit systems (Witten, Frank, 2000). In terms of accuracy, the rule sets produced by C4.5 classifiers basically contain many errors. C4.5 has limited data types and lacks the facility to label data as not applicable, (Quinlan, 1993). The decision trees produced by C4.5 are relatively bigger but these are catered for in the C5.0 version. All classification errors are treated equally but some are more serious than others and this is another disadvantage. Lack of a provision to quantify the importance of cases is a disadvantage because not all cases are equally important. Lastly, attributes need to be winnowed before a classifier is generated and C4.5 lacks this facility. KStar Algorithm classifiers Advantages Firstly, In terms of accuracy, this algorithm is comparable to C4.5 on voluminous UCI benchmark datasets. KStar performs better and with a higher speed than C4.5 on big numbers of text classifications. Thirdly, this algorithm is low in time complexity that means it is very fast. Its speed can be compared to that of naive Bayes. In addition, this algorithm can be speeded up by combining it with other scaling-up methods (Cormen et al 1990). Another advantage is that it uses entropy as a measure of distance hence providing a consistent approach to management of symbolic attributes, unknown values and continuous value attributes. The results presented compare satisfactorily with many machine learning algorithms. This algorithm is an instance-based classifier. It classifies an instance by comparison; an instance is compared to pre-classified examples stored in a database. New instances to be added to the instance database and choice of instances from the database to be used in classification are determined by a concept description updater (Gray, 1990). This helps reduce memory space requirements and also improve tolerance to noise in data. Disadvantages One of the major disadvantages of this algorithm is the fact that it has to generate distance measures for all the recorded attributes (Cormen et al 1990). Another di