CN104391970A - Attribute subspace weighted random forest data processing method - Google Patents
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Abstract
The invention discloses an attribute subspace weighted random forest data processing method. The method includes: S1, extracting N sample subsets identical to to-be-created decision trees in number from a data sample set needing training by means of sampling with replacement; S2, constructing a pruning-free decision tree model for each sample subset, when nodes of the decision tree models are constructed, adopting an information gain method for weighting all attributes taking part in node construction, and selecting M attributes with the highest weight from all the attributes to take part in node construction; S3, combining the N constructed decision tree models into a large random forest model. The information gain is used for attribute subspace weighting, and useful information can be extracted so as to improve classification precision.
Description
Technical field
The present invention relates to technical field of data processing, particularly relate to a kind of random forest data processing method of attribute subspace weighting.
Background technology
Along with constantly advancing of computing machine, internet and infotech and widely using in all trades and professions, people accumulation Various types of data become more and more huger and become increasingly complex.Such as, the attribute dimensions of the data such as various types of biological data, internet text notebook data, Digital Image Data can reach thousands of, and data volume is also in continuous increase, traditional Classification Algorithms in Data Mining is caused to be difficult to reply superelevation dimension and the ever-increasing challenge of calculated amount.
Random forests algorithm is a kind of integrated learning approach for classifying, it uses decision tree as sub-classifier, compare with other sorting algorithms, there is the advantages such as classification performance is good, precision is high, generalization ability is strong, become the algorithm that current sort research field is very popular, be widely used in the every field of data mining.Its basic thought is proposed in nineteen ninety-five by Ho the earliest, and in calendar year 2001, improvement finally becomes present random forests algorithm by Breiman.But when in the face of high dimensional data, especially sparse high dimensional data, the method for stochastic subspace that it adopts sampling can cause that the natively few useful attribute of accounting is more difficult to be drawn into, and has a strong impact on final classification results.Meanwhile, along with data volume is increasing, existing unit random forests algorithm realizes the demand that cannot meet current large data, makes outstanding algorithm cannot complete modeling in the short period of time, affects it and uses.
The main flow of existing random forests algorithm is as follows:
1) sample with putting back to from original training data N group sample, then circulation builds decision tree:
A) often organize sample and build a decision tree;
B) when building each node of decision tree, randomly drawing M attribute and carrying out node calculate;
C) in achievement process, the cutting of branch is not carried out, to the last last sample remaining;
2) constructed N number of decision-tree model is integrated into a Random Forest model.
When in the face of high dimensional data, probability is chosen in order to what improve valuable attribute, Amaratunga proposes the method for subspace sampling attribute weighting to improve the probability that when random forest is contribute, important attribute is extracted, thus the mean intensity improving decision tree reaches improvement classification performance.But, the method for be two class problems.
In existing R software systems, randomForest and party be relatively commonly use two for building the software package of random forest.RandomForest software package directly obtains after changing into C language by the Fortran source code of Breiman random forests algorithm, and have its team to safeguard.Party software package is then the random forests algorithm that the inferred conditions tree proposed by Hothorn builds.But it is when the large data for higher-dimension, all not fully up to expectations in these two consumption wrapping in time and memory source.In the R software package that existing random forest is relevant, neither one can change the selection of attribute, and all or standalone version, cannot operate in the computing environment of distributed parallel.
In sum, there is following problem in existing random forests algorithm:
Owing to adopting the mode of random sampling to choose attribute when building decision tree nodes, when in the face of superelevation dimension data, the situation having the attribute that material impact produces to result and be difficult to be selected can be caused, arithmetic accuracy is had a strong impact on;
Existing algorithm is all the mode adopting serial, decision-tree model is set up in circulation, a decision-tree model is set up in each circulation, when CPU multinuclear, cannot effectively utilize the advantage parallel computation of multiple CPU core to reach the object setting up Random Forest model fast;
When data volume gets more and more to such an extent as to single machine cannot store required data volume, existing random forests algorithm cannot all data of disposable loading, thus cannot set up accurate model;
Therefore, for above-mentioned technical matters, be necessary the random forest data processing method that a kind of attribute subspace weighting is provided.
Summary of the invention
In view of this, the object of the present invention is to provide a kind of random forest data processing method of attribute subspace weighting, to solve the problem of the large data of superelevation dimension being carried out to effectively process.
In order to achieve the above object, the technical scheme that provides of the embodiment of the present invention is as follows:
A random forest data processing method for attribute subspace weighting, described method comprises:
S1, to needing the set of data samples of training to extract with the consistent N number of sample set of the decision tree number needing to set up by the mode of sampling with replacement;
S2, the decision-tree model without beta pruning is built to each sample set, when building the node of decision-tree model, adopting information gain method to be first weighted the attribute that all participation nodes build, therefrom selecting the highest M of weight attribute participation node structure;
S3, the N number of decision-tree model built is merged into a large Random Forest model.
As a further improvement on the present invention, the decision-tree model in described step S2 adopts unit Multi-core mode or multi-host parallel distributed way to build.
As a further improvement on the present invention, the decision-tree model in described step S2 adopts unit Multi-core mode to build, and specifically comprises:
Automatic unlatching with the as many thread of CPU check figure, after each thread obtains an achievement information from process list and carry out contributing according to this information, often built a tree, just the decision-tree model built up be put in random forest;
Each thread completes the process of achievement simultaneously concurrently, until all achievement distribution of information complete, finally merges all decision trees by random forest and obtains last Random Forest model.
As a further improvement on the present invention, described step S2 also comprises:
Process list is responsible for the distribution of achievement information, after the tree of required structure has been distributed, and the situation that notice forest completes.
As a further improvement on the present invention, the decision-tree model in described step S2 adopts multi-host parallel distributed way to build, and host node is responsible for the scheduling of total volume modeling, is responsible for concrete achievement process, specifically comprises from node:
Process in host node preserves the information of all achievements, and achievement information is divided into multiple process list;
Start contributing from node on other machines as required, eachly from host node, obtains a process list from node, then independent structure decision tree generate sub-random forest on the machine of oneself;
Eachly from node, the sub-random forest built separately is put back into host node, by host node all sub-random forests is merged and obtain final Random Forest model.
As a further improvement on the present invention, when the described machine from node place is non-multinuclear machine, the serial mode of random forests algorithm is adopted to carry out modeling.
As a further improvement on the present invention, described step S2 also comprises:
When processing large data, host node cannot deposit all data messages, now, in the process list of process, just preserve the distribution situation of each deblocking on each machine, then the process of contributing, obtain required data according to distribution situation from other machines from node.
The present invention has following beneficial effect:
Information gain is used for attribute subspace weighting, useful information can be extracted, thus improve the precision of classification;
Use the multiple decision tree of the parallel structure of mode of unit multithreading, shorten the model construction time with this;
Use the mode sub-Random Forest model of distributed structure on multiple machines of the main and subordinate node of one-to-many, thus the problem that solution data cannot store on one node, improve modeling efficiency simultaneously.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, the accompanying drawing that the following describes is only some embodiments recorded in the present invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.
Fig. 1 is the schematic flow sheet of the random forest data processing method of a kind of attribute subspace weighting of the present invention.
Fig. 2 is that in the embodiment of the invention, unit Multi-core mode is contribute schematic flow sheet.
Fig. 3 is multi-host parallel distributed way achievement schematic flow sheet in the embodiment of the invention.
Embodiment
Technical scheme in the present invention is understood better in order to make those skilled in the art person, below in conjunction with the accompanying drawing in the embodiment of the present invention, technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, should belong to the scope of protection of the invention.
The invention discloses a kind of random forest data processing method of attribute subspace weighting, to solve the problem of the large data of superelevation dimension being carried out to effectively process.Its major part comprises:
1) when setting up decision tree nodes, adopting the method for attribute subspace weighting to improve and rate is chosen, to strengthen the precision property of algorithm when in the face of superelevation dimension data to useful attribute;
2) when on multi-core CPU machine, algorithm adopts the mode of parallel multithread to build decision tree, makes the same time to setting up multiple decision-tree model, can improve the time efficiency of algorithm;
3) when there being multiple stage machine can supply to calculate, algorithm adopts the mode of distributed parallel automatically to distribute on multiple stage machine the decision-tree model of required foundation, thus improves the extensibility of algorithm.
Shown in ginseng Fig. 1, the random forest data processing method of a kind of attribute subspace weighting of the present invention, is characterized in that, described in
S1, to needing the set of data samples of training to extract with the consistent N number of sample set of the decision tree number needing to set up by the mode of sampling with replacement;
S2, the decision-tree model without beta pruning is built to each sample set, when building the node of decision-tree model, adopting information gain method to be first weighted the attribute that all participation nodes build, therefrom selecting the highest M of weight attribute participation node structure;
S3, the N number of decision-tree model built is merged into a large Random Forest model.
Concrete implementation step is as follows: first, the same with existing random forests algorithm, extracts with the consistent N number of sample set of the decision tree number needing to set up to the set of data samples needing to train by the mode of sampling with replacement; Then the decision-tree model without beta pruning is built to each sample set: wherein, when building the node of decision tree, the present invention adopts information gain method to be first weighted the attribute that all participation nodes build, and therefrom selects the highest M of weight attribute and participates in node structure; Finally constructed N number of decision-tree model is merged into a large Random Forest model.
In the process building decision tree, for the environment that unit multinuclear is different with multimachine two kinds, the mode of parallel multithread and parallel distributed is adopted to build each decision-tree model respectively:
1) unit Multi-core mode builds decision-tree model
As shown in Figure 2, in process list (Task list), comprise the information of achievement, comprise the number of decision tree and the sample set corresponding to each decision tree of needs foundation.In multinuclear stand-alone environment, decision-tree model is built by the mode of multithreading is parallel, under default situations, algorithm is opened with the as many thread of CPU check figure (Thread) automatically, after each Thread obtains (fetch) achievement information from Task list and carry out contributing according to this information, often build a tree, just the decision-tree model built up has been put in random forest (Forest).
In present embodiment, Task list is also responsible for the distribution of achievement information, after the tree of required structure has been distributed, and the situation that notice Forest completes.Each Thread completes the process of achievement simultaneously concurrently like this, until all achievement distribution of information complete, finally merges all decision trees by Forest and obtains last Random Forest model.Wherein, the number of Thread is adjustable.
2) multi-host parallel distributed way sets up decision-tree model
As shown in Figure 3, when using that multiple stage machine is distributed sets up decision-tree model, will be controlled modeling process by two large modules: host node (Master node) and from node (Slave node).Wherein, Master node is responsible for the scheduling of total volume modeling, and Slave node is responsible for concrete achievement process.
Concrete steps are as follows: first, process (Tasks) in Master node preserves the information of all achievements, and achievement information is divided into multiple process list (Task list1, Task list2 ...), the Task list that its effect builds in decision-tree model with unit Multi-core mode is consistent; Then the Slave node started on other machines contributes as required, each Slave node obtains a Task list (as Task list1) from Master node, then on the machine of oneself, independently build decision tree and generate random forest Forest, its building process builds decision tree with unit Multi-core mode, if the non-multinuclear machine of this machine, so under its building process default situations, the serial mode modeling of existing random forests algorithm will be adopted; Finally, the random forest Forest built separately is put back in Master node by each Slave node, by Master node, the merging of all random forests is obtained final Random Forest model Forests.
Wherein, the number of Slave node, and the number of decision tree that each Slave node completes is controllable by Master node.In addition, when when processing large data, a node cannot deposit all data messages, now, just save the distribution situation of each deblocking on each machine in the sub-Task list of Tasks, Slave node then obtains required data according to this information from other machines in the process of contributing.
In sum, compared with prior art, the present invention has following beneficial effect:
Information gain is used for attribute subspace weighting, useful information can be extracted, thus improve the precision of classification;
Use the multiple decision tree of the parallel structure of mode of unit multithreading, shorten the model construction time with this;
Use the mode sub-Random Forest model of distributed structure on multiple machines of the main and subordinate node of one-to-many, thus the problem that solution data cannot store on one node, improve modeling efficiency simultaneously.
To those skilled in the art, obviously the invention is not restricted to the details of above-mentioned one exemplary embodiment, and when not deviating from spirit of the present invention or essential characteristic, the present invention can be realized in other specific forms.Therefore, no matter from which point, all should embodiment be regarded as exemplary, and be nonrestrictive, scope of the present invention is limited by claims instead of above-mentioned explanation, and all changes be therefore intended in the implication of the equivalency by dropping on claim and scope are included in the present invention.Any Reference numeral in claim should be considered as the claim involved by limiting.
In addition, be to be understood that, although this instructions is described according to embodiment, but not each embodiment only comprises an independently technical scheme, this narrating mode of instructions is only for clarity sake, those skilled in the art should by instructions integrally, and the technical scheme in each embodiment also through appropriately combined, can form other embodiments that it will be appreciated by those skilled in the art that.
Claims (7)
1. a random forest data processing method for attribute subspace weighting, is characterized in that, described method comprises:
S1, to needing the set of data samples of training to extract with the consistent N number of sample set of the decision tree number needing to set up by the mode of sampling with replacement;
S2, the decision-tree model without beta pruning is built to each sample set, when building the node of decision-tree model, adopting information gain method to be first weighted the attribute that all participation nodes build, therefrom selecting the highest M of weight attribute participation node structure;
S3, the N number of decision-tree model built is merged into a large Random Forest model.
2. method according to claim 1, is characterized in that, the decision-tree model in described step S2 adopts unit Multi-core mode or multi-host parallel distributed way to build.
3. method according to claim 2, is characterized in that, the decision-tree model in described step S2 adopts unit Multi-core mode to build, and specifically comprises:
Automatic unlatching with the as many thread of CPU check figure, after each thread obtains an achievement information from process list and carry out contributing according to this information, often built a tree, just the decision-tree model built up be put in random forest;
Each thread completes the process of achievement simultaneously concurrently, until all achievement distribution of information complete, finally merges all decision trees by random forest and obtains last Random Forest model.
4. method according to claim 3, is characterized in that, described step S2 also comprises:
Process list is responsible for the distribution of achievement information, after the tree of required structure has been distributed, and the situation that notice forest completes.
5. method according to claim 2, is characterized in that, the decision-tree model in described step S2 adopts multi-host parallel distributed way to build, and host node is responsible for the scheduling of total volume modeling, is responsible for concrete achievement process, specifically comprises from node:
Process in host node preserves the information of all achievements, and achievement information is divided into multiple process list;
Start contributing from node on other machines as required, eachly from host node, obtains a process list from node, then independent structure decision tree generate sub-random forest on the machine of oneself;
Eachly from node, the sub-random forest built separately is put back into host node, by host node all sub-random forests is merged and obtain final Random Forest model.
6. method according to claim 5, is characterized in that, when the described machine from node place is non-multinuclear machine, adopts the serial mode of random forests algorithm to carry out modeling.
7. method according to claim 5, is characterized in that, described step S2 also comprises:
When processing large data, host node cannot deposit all data messages, now, in the process list of process, just preserve the distribution situation of each deblocking on each machine, then the process of contributing, obtain required data according to distribution situation from other machines from node.
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| CN110108992B (en) * | 2019-05-24 | 2021-07-23 | 国网湖南省电力有限公司 | Cable partial discharge fault identification method and system based on improved random forest algorithm |
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