Accounts Receivable Seamless Prediction for Companies by Using Multiclass Data Mining Model

Most companies find themselves in highly competitive markets nowadays. As a result, many companies struggle to manage their financial obligation to pay their supplier on time. Delayed payments to suppliers can create all kinds of issue with the supplier's cash flow. Finding a way to reduce or avoid any potential losses because of this delay is needed. Currently, data mining techniques have been widely applied to the assessment or prediction of credit scores for customers in the banking industry (credit scoring), and the most commonly used method is classification. Based on previous studies, research has been conducted to develop a data mining model to produce the best classification model to predict a customer’s payment capabilities. With the application of data mining approaches using oversampling, feature selection (FS) algorithm, including Relief, Information Gain Ratio, PCA, and multiclass algorithm, including Random Forest, SVM, One-versus-all, All-versus-all and Error Correcting Output Coding (ECOC), is expected to produce good accuracy to predict the ability of these payments. As a result of this research, the model proposed can provide the best classification model with 84.24% accuracy and AUC value of 95.3% using sample dataset of manufacturing industry within three years perio

Multi-test Decision Tree and its Application to Microarray Data Classification

Objective: The desirable property of tools used to investigate biological data is easy to understand models and predictive decisions. Decision trees are particularly promising in this regard due to their comprehensible nature that resembles the hierarchical process of human decision making. However, existing algorithms for learning decision trees have tendency to underfit gene expression data. The main aim of this work is to improve the performance and stability of decision trees with only a small increase in their complexity. Methods: We propose a multi-test decision tree (MTDT); our main contribution is the application of several univariate tests in each non-terminal node of the decision tree. We also search for alternative, lower-ranked features in order to obtain more stable and reliable predictions. Results: Experimental validation was performed on several real-life gene expression datasets. Comparison results with eight classifiers show that MTDT has a statistically significantly higher accuracy than popular decision tree classifiers, and it was highly competitive with ensemble learning algorithms. The proposed solution managed to outperform its baseline algorithm on $14$ datasets by an average $6$ percent. A study performed on one of the datasets showed that the discovered genes used in the MTDT classification model are supported by biological evidence in the literature. Conclusion: This paper introduces a new type of decision tree which is more suitable for solving biological problems. MTDTs are relatively easy to analyze and much more powerful in modeling high dimensional microarray data than their popular counterparts

Algorithms Implemented for Cancer Gene Searching and Classifications

Understanding the gene expression is an important factor to cancer diagnosis. One target of this understanding is implementing cancer gene search and classification methods. However, cancer gene search and classification is a challenge in that there is no an obvious exact algorithm that can be implemented individually for various cancer cells. In this paper a research is con-ducted through the most common top ranked algorithms implemented for cancer gene search and classification, and how they are implemented to reach a better performance. The paper will distinguish algorithms implemented for Bio image analysis for cancer cells and algorithms implemented based on DNA array data. The main purpose of this paper is to explore a road map towards presenting the most current algorithms implemented for cancer gene search and classification