Mixed-attribute Datasets Research Articles

A new filter-based gene selection approach in the DNA microarray domain

The high dimensionality of data hinders the learning ability of machine learning algorithms. Feature selection techniques can be used to reduce dimensionality, which is an important step for processing high-dimensional data. Feature selection solves this problem by removing irrelevant and redundant information, which can improve learning models, reduce calculation time, and improve learning accuracy. In this paper, a novel filter in mixed-attribute datasets for feature selection is proposed. The independent attributes are mixed or heterogeneous in the sense that both numerical and categorical attribute types may appear together in the same dataset. Based on the preordonnances theory, we use a new concept to quantify the relevance and redundancy of features even if there are heterogeneous (mixed-type) data. The technique for order preference by similarity to the ideal solution is one of the well-known multicriteria decision-making methods; it is utilized as a weighting and informative feature selection filter. To assess the effectiveness of the proposed method, several experiments, both simulated and real, are performed, including a comparison to other well-known filter methods. The experimental results show that, in most cases, the method yielded competitive results in comparison to other methods.

A Hybrid Method to Measure Distribution Consistency of Mixed-Attribute Datasets

Random sample partition (RSP) is a newly developed data management and processing model for Big Data processing and analysis. To apply the RSP model for Big Data computation tasks, it is very important to measure the distribution consistency of different datasets. Existing measurement methods for continuous-attribute and discrete-attribute datasets cannot directly deal with mixed-attribute datasets. In this article, we design a hybrid method to measure the distribution consistency among different mixed-attribute datasets by using a multilayer extreme learning machine (MLELM) and the generalized maximum mean discrepancy (GMMD) criterion, abbreviated as MLELM-GMMD. MLELM is first used to transform original mixed-attribute datasets into corresponding deep encoding datasets. Then, the GMMD criterion is applied to check the distribution consistency of the deep encoding datasets. Four experiments have been done to validate the feasibility and effectiveness of MLELM-GMMD, i.e., the impact of MLELM on the amount of information during mixed-attribute data transformation, the impact of MLELM on distributions of mixed-attribute data, the distribution consistencies of RSP and non-RSP data blocks, and the comparison with other measurement methods. Experimental results show that the proposed MLELM-GMMD method can measure the distribution consistency of mixed-attribute datasets more accurately than one-hot encoding-based methods.

A novel dependency-oriented mixed-attribute data classification method

How to design an efficient method to handle mixed-attribute data classification (MADC) problems has become a hot topic in data mining and machine learning. Current MADC methods mostly transform mixed-attribute data into discrete-attribute data or continuous-attribute data before classification algorithms are trained. The discretization of continuous-attribute data usually results in information loss, while the binarization of discrete-attribute data generally yield more discrete-attributes. To address these issues, this paper proposes a novel MADC method abbreviated as DO-RVFL-NBC, which is a Dependency-Oriented aggregation model of random vector functional link (RVFL) network and naive Bayes classifier (NBC). First, the method transforms the original mixed-attribute set into a dependent attribute set and an independent attribute set by considering the variation rates of dependence and independence, respectively. Second, a RVFL network is trained based on the dependent attribute set where each attribute has a weight to represent its dependence importance degree. Third, a weighted NBC is constructed by assigning the independence importance degrees as weights for the calculation of class-conditional probability. Finally, exhaustive experiments are conducted to validate the feasibility, rationality, and effectiveness of the DO-RVFL-NBC method using 22 benchmark mixed-attribute data sets. Experimental results show that (1) dependence and independence exist in the original mixed-attribute set and can be effectively explored; (2) changes of attribute dependences can improve the generalization capabilities of the RVFL network and NBC; and (3) a statistical analysis indicates that DO-RVFL-NBC can obtain considerably better testing accuracies on the benchmark mixed-attribute data sets in comparison with 13 other MADC methods. This demonstrates that DO-RVFL-NBC is a viable approach for MADC problems.

A new method for measuring the distribution consistency of mixed-attribute data sets

The measurement of data distribution consistency is a key problem in the process of generating random sample partition (RSP) of big data. How to measure the distribution consistency of mixed-attribute data sets reasonably and effectively is the focus of current research on RSP technology. This paper proposes a new method to measure the distribution consistency of mixed-attribute data sets based on deep encoding and maximum mean discrepancy (DE-MMD). Firstly, we conduct the one-hot encoding to transform the original data set with discrete attributes into the one-hot encoding data set. Then, we construct and train an autoencoder with single hidden layer based on the one-hot encoding data and thus we get the corresponding deep encoding data set by representing the original data set with hidden layer output. Finally, we measure the distribution consistency of mixed-attribute data sets based on the corresponding deep encoding data sets by using the maximum mean discrepancy index. On 4 benchmark mixed-attribute data sets, which are Adult、 Australian、 CRX and German, we compare the measure performances of DE-MMD method with those of the one-hot encoding-based MMD (OE-MMD) method and the binarization-based similarity measure (BSM) method. The experimental results show that the proposed method can measure the distribution consistency of mixed-attribute data sets more accurately and more effectively than OE-MMD and BSM methods.

Scalability achievements for enumerative biclustering with online partitioning: Case studies involving mixed-attribute datasets

Biclustering is a powerful data analysis technique and its concept is appealing in many domains, such as natural sciences and market basket analysis. To exemplify the wide range of biclustering applications, we can also mention recommender systems, educational data mining, emerging topic detection and counterfeit product detection. In this paper, we further extend RIn-Close_CVC, a biclustering algorithm capable of performing, in numerical datasets, an efficient, complete, correct and non-redundant enumeration of maximal biclusters with constant values on columns. By avoiding a priori partitioning and itemization of the dataset, RIn-Close_CVC implements an online partitioning, which is demonstrated here to guide to more informative biclustering results. The improved algorithm, called RIn-Close_CVC3, is characterized by: a drastic reduction in memory usage; a consistent gain in runtime; additional ability to handle datasets with missing values; and new skills to operate with attributes characterized by distinct distributions or even mixed data types. Moreover, RIn-Close_CVC3 keeps those four attractive properties of RIn-Close_CVC, as formally proved here. The experimental results include synthetic and real-world datasets used to perform scalability and sensitivity analyses, besides a comparative inquiry involving a priori and online partitioning. As a practical case study, a parsimonious set of relevant and interpretable mixed-attribute-type rules is obtained in the context of supervised descriptive pattern mining.

Spark-GHSOM: Growing Hierarchical Self-Organizing Map for large scale mixed attribute datasets

The Growing Hierarchical Self-Organizing Map (GHSOM) algorithm has shown its potential for performing several tasks such as exploratory analysis, anomaly detection and forecasting on a variety of domains including the financial and cyber-security domains. GHSOM is a dynamic variant of the SOM algorithm which generates a multi-level hierarchy of SOM maps based solely on input data. However, in order to generate this multi-level structure, GHSOM requires multiple iterations over the input dataset, thus making it intractable on large datasets. Moreover, the conventional GHSOM algorithm is designed to handle datasets with numeric attributes only. This represents an important limitation as most modern real-world datasets are characterized by mixed attributes - numerical and categorical. In this work, we propose an extension of the conventional GHSOM algorithm called Spark-GHSOM, which exploits the Spark platform to process massive datasets in a distributed manner. Moreover, we leverage a method known as the distance hierarchy approach to modify the optimization function of GHSOM so that it can (also) coherently handle mixed-attribute datasets. We test our new method with respect to accuracy, scalability and descriptive power. The results obtained using different datasets demonstrate the superior predictive and descriptive capabilities of Spark-GHSOM, as well as its applicability to large-scale datasets which could not be analyzed before.

Heuristically repopulated Bayesian ant colony optimization for treating missing values in large databases

The incomplete datasets with missing values are unsuitable for making strategic decisions since they lead to biased results. This problem is even worse when the dataset is large and collected from many heterogeneous sources. The paper deals with missing scenarios which were not dealt together earlier. The proposed Dual Repopulated Bayesian Ant Colony Optimization (DPBACO) handles both ignorable and non-ignorable missing values in heterogeneous attributes of large datasets The DPBACO integrates Bayesian principles with Ant Colony Optimization technique since both are simple and efficient to implement. After pheromone updation, repopulation of the solution pool is done by dividing the population into two based on their fitness values and generating new offsprings by performing crossover operation. The DPBACO algorithm is implemented on six large mixed-attribute datasets for imputing both kinds of missing values. The empirical and statistical results show that DPBACO performs better than other existing methods at variable missing rates ranging from 5% to 50%.

Clustering Mixed-Attribute Data using Random Walk

Most clustering algorithms rely in some fundamental way on a measure of either similarity or distance — either between objects themselves, or between objects and cluster centroids. When the dataset contains mixed attributes, defining a suitable measure can be problematic. This paper presents a general graph-based method for clustering mixed-attribute datasets that does not require any explicit measure of similarity or distance. Empirical results on a range of well-known datasets using a range of evaluation measures show that the method achieves performance competitive with traditional clustering algorithms that require explicit calculation of distance or similarity, as well as with more recently proposed clustering algorithms based on matrix factorization.

Multi-objective genetic algorithm for missing data imputation

A large number of techniques for data analyses have been developed in recent years, however most of them do not deal satisfactorily with a ubiquitous problem in the area: the missing data. In order to mitigate the bias imposed by this problem, several treatment methods have been proposed, highlighting the data imputation methods, which can be viewed as an optimization problem where the goal is to reduce the bias caused by the absence of information. Although most imputation methods are restricted to one type of variable whether categorical or continuous. To fill these gaps, this paper presents the multi-objective genetic algorithm for data imputation called MOGAImp, based on the NSGA-II, which is suitable for mixed-attribute datasets and takes into account information from incomplete instances and the modeling task. A set of tests for evaluating the performance of the algorithm were applied using 30 datasets with induced missing values; five classifiers divided into three classes: rule induction learning, lazy learning and approximate models; and were compared with three techniques presented in the literature. The results obtained confirm the MOGAImp outperforms some well-established missing data treatment methods. Furthermore, the proposed method proved to be flexible since it is possible to adapt it to different application domains.

On Constructing Static Evaluation Function using Temporal Difference Learning

An Outlier is a data point which is significantly different from the remaining data points. Outlier is also referred as discordant, deviants and abnormalities. Outliers may have a particular interest, such as credit card fraud detection, where outliers indicate fraudulent activity. Thus, outlier detection analysis is an interesting data mining task, referred to as outlier analysis. Detecting outliers efficiently from dataset is an important task in many fields like Credit card Fraud, Medicine, Law enforcement, Earth Sciences etc. Many methods are available to identify outliers in numerical dataset. But there exist limited number of methods are available for categorical and mixed attribute datasets. In the proposed work, a novel outlier detection method is proposed. This proposed method finds anomalies based on each record’s “multi attribute outlier factor through correlation” score and it has great intuitive appeal. This algorithm utilizes the frequency of each value in categorical part of the dataset and correlation factor of each record with mean record of the entire dataset. This proposed method used Attribute Value Frequency score (AVF score) concept for categorical part. Results of the proposed method are compared with existing methods. The Bank data (Mixed) is used for experiments in this paper which is taken from UCI machine learning repository.
 Keyword: Outlier, Mixed Attribute Datasets, Attribute Value Frequency Score

Missing Value Estimation for Mixed-Attribute Data Sets

Missing data imputation is a key issue in learning from incomplete data. Various techniques have been developed with great successes on dealing with missing values in data sets with homogeneous attributes (their independent attributes are all either continuous or discrete). This paper studies a new setting of missing data imputation, i.e., imputing missing data in data sets with heterogeneous attributes (their independent attributes are of different types), referred to as imputing mixed-attribute data sets. Although many real applications are in this setting, there is no estimator designed for imputing mixed-attribute data sets. This paper first proposes two consistent estimators for discrete and continuous missing target values, respectively. And then, a mixture-kernel-based iterative estimator is advocated to impute mixed-attribute data sets. The proposed method is evaluated with extensive experiments compared with some typical algorithms, and the result demonstrates that the proposed approach is better than these existing imputation methods in terms of classification accuracy and root mean square error (RMSE) at different missing ratios.

Autonomous clustering using rough set theory

This paper proposes a clustering technique that minimizes the need for subjective human intervention and is based on elements of rough set theory (RST). The proposed algorithm is unified in its approach to clustering and makes use of both local and global data properties to obtain clustering solutions. It handles single-type and mixed attribute data sets with ease. The results from three data sets of single and mixed attribute types are used to illustrate the technique and establish its efficiency.