It is the goal of classification and regression to build a data mining model that can be used for prediction. To construct such a model, we are given a set of training records, each having several attributes. These attributes can either be numerical (for example, age or salary) or categorical (for example, profession or gender). There is one distinguished attribute, the dependent attribute; the...

In this paper, we investigate potential factors that may influence business performance on Yelp. We considered businesses’ overall star ratings as a measure of their performance. In order to account for user sentiment and location dynamics we constructed additional features from business and user review data. We experimented with regression (Linear and Decision-Tree) as well as classification (...

We consider the classification problem as a problem of approximation of a given training set. This approximation is constructed in a multiresolution framework, and organized in a tree-structure. It allows efficient training and query, both in constant time per training point. The proposed method is efficient for low-dimensional classification and regression estimation problems with large data s...

Building classification models plays an important role in DNA mircroarray data analyses. An essential feature of DNA microarray data sets is that the number of input variables (genes) is far greater than the number of samples. As such, most classification schemes employ variable selection or feature selection methods to pre-process DNA microarray data. This paper investigates various aspects of...

This paper presents a data envelopment analysis (DEA) model combined with Bootstrapping to assess performance of one of the Data mining Algorithms. We applied a two-step process for performance productivity analysis of insurance branches within a case study. First, using a DEA model, the study analyzes the productivity of eighteen decision-making units (DMUs). Using a Malmquist index, DEA deter...

decision trees as one of the data mining techniques, is used in credit scoring of bank customers. the main problem is the construction of decision trees in that they can classify customers optimally. this paper proposes an appropriate model based on genetic algorithm for credit scoring of banks customers in order to offer credit facilities to each class. genetic algorithm can help in credit sco...