Abstract
The regression problem is a valued problem in the domain of machine learning, and it has been widely employed in many fields such as meteorology, transportation, and material. Granular computing (GrC) is a good approach of exploring human intelligent information processing, which has the superiority of knowledge discovery. Ensemble learning is easy to execute parallelly. Based on granular computing and ensemble learning, we convert the regression problem into granular space equivalently to solve and proposed boosted fuzzy granular regression trees (BFGRT) to predict a test instance. The thought of BFGRT is as follows. First, a clustering algorithm with automatic optimization of clustering centers is presented. Next, in terms of the clustering algorithm, we employ MapReduce to parallelly implement fuzzy granulation of the data. Then, we design new operators and metrics of fuzzy granules to build fuzzy granular rule base. Finally, a fuzzy granular regression tree (FGRT) in the fuzzy granular space is presented. In the light of these, BFGRT can be designed by parallelly combing multiple FGRTs via random sampling attributes and MapReduce. Theory and experiments show that BFGRT is accurate, efficient, and robust.
Highlights
Learning ability is the basic feature of human intelligence
(ii) Second, parallel fuzzy granulation of data based on the above clustering algorithm combined with MapReduce is presented, which solves the problem of high complexity of traditional granulation and enhances granulation efficiency (iii) ird, we define fuzzy granules and related metric operators, design a loss function, construct an individual fuzzy granular regression tree in the granular space by optimizing the loss function, and parallelly integrate multiple fuzzy granular regression trees built by different attributes into a stronger learner based on MapReduce to accurately solve the regression problem
From the dataset containing 1% noise, as shown in Figure 6(b), the maximum value of root mean square error (RMSE) of boosted fuzzy granular regression trees (BFGRT) is 1.94, which has improved by about 16.01%, 9.77%, and 0.51%, respectively, compared with support vector regression (SVR), random forest (RF), and long short-term memory (LSTM)
Summary
Learning ability is the basic feature of human intelligence. Prediction is the ability of humans to judge the future based on learning, and it is a concrete manifestation of human learning ability. Avnimelech and Intrator introduced the above concepts into the regression problem and gave a proof of the equivalence of the strong and weak learning of the one [3] Another major theoretical basis for ensemble learning is the “No free lunch” theory proposed by Wolpert [4]. Polkowski and Skowron adopted the rough mereology method, neural network technology, and the idea of knowledge granulation to design a rough neural computing model, which combines the division block of the rough set and the neural network to form an efficient neural computing method [29] Peters and his colleagues employed the indistinguishable relationship to divide the real number into multiple subintervals and divided a whole area into several grid units, and each unit was regarded as a granule, and proposed metric is between two information granules on the adjacent relationship and the containment relationship, respectively [30]. We adopt parallel granulation and ensemble learning based on MapReduce to solve the regression problem from granular computing angle and enhance the performance of regression and efficiency of granulation
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