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1. AbstractThe objective of this paper is to present the process of building a model for identifying the right combination of inputs for optimizing the Concrete Compressive Strength. Multiple machine learning algorithms were evaluated. A process of optimizing the solution using Ensemble Learning was identified and successfully tested. These are meta-heuristic techniques used to improve and combine the predictions of multiple learning algorithms.The model building was done in the following stages:
- Models were built with different number of hidden layers of Multilayer Perceptrons and evaluated. The deep learning ones with 3 hidden layers generally performed well.
- An ensemble of the best performing Multilayer Perceptrons was tested and was found to significantly improve the performance.
- Other Regression Models were tested. Some of them were Ensemble models.
- An Ensemble of all the best performing models was also tested and it showed significantly improved results. Some of the models included were Ensemble ones themselves
- This ensemble was passed as an input to a chain of ensembles and this improved further the performance.
- Finally a search was made to find the combination of input parameters that would maximise the Concrete Compressive Strength using Turing Point’s GA optimizer
Here is the link to the paper
©Copyright Turing Point Pty Ltd
Material in this paper may not be used for commercial purposes without the written permission of Turing Point Pty Ltd.
2. IntroductionMachine Learning (ML), a branch of Computer Science that focuses on drawing insights and conclusions by examining data sets, is an increasingly popular discipline today in resolving enterprise business issues. However the field is vast and consists of numerous algorithms and approaches. Data sets are also often complex and require to be pre-processed before an ML algorithm can be 'trained' to learn from such data. For a particular problem domain and data set, defining the pre-processing technique and selecting the ML algorithm (or set of algorithms) is still largely 'an art rather than a science' depending on the knowledge and skills of the expert/data scientist in question. With time this will change and scientific guiding principles/best practices will emerge to pre-process data and to select appropriate algorithms for a particular problem domain - as the discipline matures.
In the meanwhile we have conducted a study of applying the so-called 'ensemble learning' approach to a data-set.
2.1 What is Ensemble Learning?Ensemble learning is the process by which multiple models, such as classifiers or experts, are strategically generated and combined to solve a particular computational intelligence problem. It has been found that an ensemble can often perform better than a single model. This process is similar to important decisions we take in day to day situations.
An investment in equity may require consulting multiple analysts for their expert opinion. Each one may look at it from a different angle. It may also be good to consult the opinion of friends and relatives. Finally a consensus decision is taken.
The election of an office bearer from potential candidates is the result of the maximum number of votes cast by the members.
One may need to consult multiple real estate analysts to decide on the right price for a property.
The individual models contributing to a decision may differ due to a number of factors:
- The algorithms used in building the model
- The training samples used to build the model
- There may be a difference in hypothesis
- The initial seed in model building may be different
- A combination of the all the items listed above.
2.2 Building Machine Learning ModelsThe building of a Machine Learning model is a complex process. A right algorithm or an ensemble of them needs to be chosen from a plethora of available algorithms.
The output of the model can be broad classification like trying to identify the type of car from the features, or it can be a continuous or Regression value instead of being discrete items. Often the solution depends on the complexity of the problem being addressed. In some situations a simple linear model may be sufficient but in other situations a complex combination may be warranted.
The Concrete Compressive Strength use case being addressed by this paper is a complex Regression problem. Hence it required only algorithms that can address a problem of this type. The model selection process was addressed in 4 stages.
- Multilayer Perceptrons especially the deep learning models can be used for any complex models. But they need to be configured for number of hidden layers and the neurons per layer. Trying out different model configurations was addressed in the first stage
- The second stage involved an Ensemble of best performing Multilayer Perceptrons
- The other popular Regression algorithms were evaluated during stage 3.
- The final stage involved an ensemble of all the best performing algorithms, some of them were ensembles themselves. The mean of the selected models was then appended to the attributes and passed to a chain of Ensemble models.
3. Concrete Strengthening ProcessThe purpose of this paper is to build a Regression Model for the Concrete Strengthening Process. The description of the process and the data set can be found in the following link: http://archive.ics.uci.edu/ml/datasets/Concrete+Compressive+Strength This is a free and a complex dataset available from the Machine Learning Repository of Centre of Machine Learning and Intelligent Systems at University of California Irvine Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients. These ingredients include cement, blast furnace slag,
fly ash, water, superplasticizer, coarse aggregate, fine aggregate and age. The following are the list of data attributes. The Concrete Compressive Strength is the last attribute which is the desired output combining
the inputs
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Figure 1 illustrates the Concrete Compressive Strength Process
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Figure 1 Block Diagram of Concrete Compressive Strength Process
The objective is to model Concrete Compressive Strength as a function of these input variables.The dataset contains 1030 measurements.
4. The Process of building and verifying Machine Learning Systems
The objective of any machine learning systems is to emulate the real time behavior as a function of the independent variables or predictors. In order to do this the behavior is modeled with some training samples and verified against some test samples and released with the hope that the resulting solution will do a perfect job predicting the outcome of any unseen test data. The confidence in the model will be high if the training data contained samples representative of all the variations of the real world. However, there can be practical limitations in getting data sets. It may not be possible to get samples of all possible variations thereby constrain the perfection of the model
4.1 Training and Test SplitHence it is possible to work only what is available with right processes in place to build as perfect a model as possible. Assumptions have to be made that the data is independent and identically distributed. The data set is randomly split into training and test data. The test data is used for verifying the performance only and is not to be used for any model building process. Typical split ratios are 60:40, 70:30, 80:20 or even 90:10.
For our model this ratio has been deliberately kept at 50:50 in order to increase the confidence in the resulting model. The 1030 tuples of data set was split randomly into Training and test sets each having 515 tuples. The test data was kept aside and used only for testing purpose. No change was done to the models after testing
4.2 Bias and Variance A bias is the difference between the expected value and the actual value of a variable. This is an important measure for a machine learning which is concerned with predicting dependent or target outcomes from independent variables or predictors. Thus if “y” represents the actual value and “E(y’)” the estimated value then
Variance of an estimator y’ is the expected value of the square of the difference from its mean E(y’). Thus
Ideally in a perfect world one would seek a model with zero bias and zero variance. But this is hardly the case. A model may be trained to have a low bias with the training data but can perform poorly resulting in high variance with the test data. In such a situation , the estimator is considered to be over-fitting to the training data including the noise in it as well. On the other hand, a model may have a high bias in which case may be simpler and under-fit the training data but may have a relatively lower variance with test data.
Hence there always has always to be bias – variance trade off, in that the bias need not be too low with training data that would result in high variance with test data. This calls for a fairly complex model.
4.3 Model Selection ProcessGiven these requirements of relatively low bias and not so high variance on test data, the next step is to evaluate models and compare their performance. Multilayer Perceptrons perform well in complex situations and it was decided to try out deep learning models various combinations of hidden layers and compare their performances. Ensemble learners are found to improve the performance of the base models and are able to meet bias-variance requirements. Other algorithms were to be evaluated as well and have the performances compared. The following is the summary of the Model Selection Process:
4.3.1 Train Multilayer Perceptrons with different configurations of input layers4.3.2 Combine the best MLP performers into an Ensemble4.3.3 Train other known algorithms from Weka and evaluate their performance4.3.4 Combine the best performers from all the models including Multilayer Perceptrons into an ensemble4.3.5 Pass the ensemble as another attribute to a chain of ensembles and test performance4.4 Development ProcessThe entire development process was carried out in java using Weka libraries as there was a need to train and store the models and develop comparison reports
4.5 Cleaning up Training DataThe training data was cleaned up to eliminate the noise and other redundant information in order to optimize the training time. After this process the number of tuples were reduced from 515 to 481.
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