CÓDIGO HUMEDAD PISO TERMICO PORCENTAJE HAS

This subsection is about the neural network algorithms and software programs used in this study.

Although this study aims to emphasize significance of the preprocessing of IAL, it is still

necessary to give a brief introduction on the kind of predictive method and the kind of programs

that have been employed in this research. The experimental methodology used here should be the

same as the one in previous studies, so that the results impacted by the preprocessing in this study

and those derived in previous research can be acceptable and comparable. Therefore, the working

procedure of neural network and software programs should be fixed and unchangeable during the

whole pattern recognition process.

3.4.1

Neural Networks and Prediction Methods

As we know, neural networks have exhibited a wonderful performance in pattern recognition. It

usually employs supervised machine learning strategy to cope with classification and regression

problems. As neural networks can work in an evolutionary way [48, 49] which matches the

characters of IAL where features are trained incrementally, this study will also employ neural

networks to tackle with prediction tasks.

Neural networks have a number of variants. In this study, Resilient Backpropagation (RPROP) algorithm is employed with ITID [4], as this has been adopted in most previous IAL studies for many times, and it is also necessary to keep the consistency between new predictive

approach and previous approaches. More specifically, features of datasets in classification or

regression problems are arranged to be imported into the prediction system like neural networks

one by one or group by group according to the strategy of ITID. When one or some new features

are imported, they will be trained in a constructive way using RPROP. This constructive way in

our research is based on ILIA1 or ILIA2 [21], and RPROP in such a constructive way also can be

regarded as a variant of Constructive Backpropagation (CBP). In previous research, CBP has played a successful role in pattern classification [39, 50].

RPROP is a heuristic supervised machine learning approach in feedforward artificial neural

METHODOLOGY

mechanisms. It was firstly developed by Martin Riedmiller and Heinrich Braun in 1992 [51]. In

this study, all the parameters of RPROP are set to be the same as those in previous IAL studies [4,

21]. Moreover, the stopping criteria are also the same as those presented in previous studies [36].

In this study, an early stopping criteria is employed. As we know, constructive learning

algorithms have many advantages [52-57], however, they are very sensitive to change in the

stopping criteria [21]. Neural networks may not generate good results, if training is too short.

However, if training is too long, it will spend much computation time and may get overfitting and

poor generalization. By referring to [36, 52], the method of early stopping using a validation set

to prevent overfitting is adopted. The detailed introduction to parameter setting and stopping

criteria is presented in Appendix C.

3.4.2

Programs of IAL based on Neural Networks

In this study, the prediction system based on neural networks was developed by C++. Some basic

codes such as foundation classes Base_Node, Base_Link, is from the book “Object-oriented

neural networks in C++” [58]. Other advanced classes were developed based on these basic

classes. Figure 3.3 shows the class hierarchy in RPROP IAL code. Our program uses

“winner-takes-all” to predict the final results. When the program is launched, the initial network is firstly built. After that, if the initial networks cannot get the acceptable generalization

performance, the hidden units will be added. When a hidden unit is added, a pool of candidates

should be trained and the best one is selected.

In our experiments, error rates derived by ITID (ILIA1) and ITID (ILIA2) will be used as

final results for comparison. This IAL program can produce five different prediction results,

which are derived from ITID with ILIA1, 2, 3, 4, and 5, respectively. The reasons why results

from ITID (ILIA1) and ITID (ILIA2) are chosen are: firstly, referring to [21], “ILIA2 algorithm

is better than the other ILIA algorithms. ” Secondly, previous research often employs results from

ILIA1 and ILIA2 as the formal final results for comparison [4]. Therefore, error rates derived by

ITID (ILIA1) and ITID (ILIA2) are chosen for comparison in the last stage. Further, in this thesis,

except error rates derived by ITID (ILIA1) and ITID (ILIA2), the average value of ITID (ILIA1)

are related to each other and thus the average value can be a representative to measure the

stability of the performance of feature ordering with ITID-ILIA algorithms. Some further

information about the program operation used in this study is presented in Appendix D.

Base_Node Input_Node Bias_Node Feed_Forward_Node BP_Node BP_Output_Node BP_Middle_Node Rprop_Output_Node Rprop_Middle_Node Base_Link BP_Link Epoch_BP_Link Rprop_Link

Figure 3.4: Class Hierarchy in RPROP IAL

3.5

Summary

This chapter mainly presented the methodologies about experimental data descriptions and IAL

predictive approaches used in this study. To make the results derived from this study can be

compared with those from previous studies, datasets used in this study are identically collected

from UCI Machine Learning Repository. Moreover, based on UCI datasets, conventional data

preparation like data sampling and case reduction is carried out. All the datasets are divided into

three parts: training, validation and testing. Based on these division of datasets, IAL neural

networks like ITID can be employed for pattern classification and regression. During the

METHODOLOGY

stable, so that the stage of preprocessing can be treated as the only source for all the fluctuations

derived from final results by different approaches. In the forthcoming chapters, novel feature

ordering metrics and approaches will be presented and compared. All of the later works will be