下面介紹兩種之前競賽使用到的特徵選擇方案

方案一

• 流程圖
  

以上方法使用方差、卡方檢驗、決策樹模型輸出特徵重要性方法綜合起來進行特徵選擇，該方案在馬上AI全球挑戰賽中發揮了比較大的作用。該連結是我們的解決方案，開源是一種精神，僅供大家共同學習交流。

• python程式碼實現

#coding=utf-8

import numpy as np
import pandas as pd

'''單變數特徵選取'''
from sklearn.feature_selection import SelectKBest, chi2
'''去除方差小的特徵'''
from sklearn.feature_selection import
 
 VarianceThreshold
'''迴圈特徵選取'''
from sklearn.svm import SVC
from sklearn.feature_selection import RFE
'''RFE_CV'''
from sklearn.ensemble import ExtraTreesClassifier


class FeatureSelection(object):
    def __init__(self, feature_num):
        self.feature_num = feature_num
        self.train_test, self.label, self.test = self.read_data()    # features #
 

        self.feature_name = list(self.train_test.columns)     # feature name #

    def read_data(self):
        test = pd.read_csv(r'test_feature.csv', encoding='utf-8')
        train_test = pd.read_csv(r'train_test_feature.csv', encoding='utf-8')
        print('讀取資料完畢。。。')
        label = train_test[['target'
 
]]
        test = test.iloc[:, 1:]
        train_test = train_test.iloc[:, 2:]
        return train_test, label, test

    def variance_threshold(self):
        sel = VarianceThreshold()
        sel.fit_transform(self.train_test)
        feature_var = list(sel.variances_)    # feature variance #
        features = dict(zip(self.feature_name, feature_var))
        features = list(dict(sorted(features.items(), key=lambda d: d[1])).keys())[-self.feature_num:]
        # print(features)   # 100 cols #
        return set(features)   # return set type #

    def select_k_best(self):
        ch2 = SelectKBest(chi2, k=self.feature_num)
        ch2.fit(self.train_test, self.label)
        feature_var = list(ch2.scores_)  # feature scores #
        features = dict(zip(self.feature_name, feature_var))
        features = list(dict(sorted(features.items(), key=lambda d: d[1])).keys())[-self.feature_num:]
        # print(features)     # 100 cols #
        return set(features)    # return set type #

    def svc_select(self):
        svc = SVC(kernel='rbf', C=1, random_state=2018)    # linear #
        rfe = RFE(estimator=svc, n_features_to_select=self.feature_num, step=1)
        rfe.fit(self.train_test, self.label.ravel())
        print(rfe.ranking_)
        return rfe.ranking_

    def tree_select(self):
        clf = ExtraTreesClassifier(n_estimators=300, max_depth=7, n_jobs=4)
        clf.fit(self.train_test, self.label)
        feature_var = list(clf.feature_importances_)  # feature scores #
        features = dict(zip(self.feature_name, feature_var))
        features = list(dict(sorted(features.items(), key=lambda d: d[1])).keys())[-self.feature_num:]
        # print(features)     # 100 cols #
        return set(features)  # return set type #

        def return_feature_set(self, variance_threshold=False, select_k_best=False, svc_select=False, tree_select=False):
        names = set([])
        if variance_threshold is True:
            name_one = self.variance_threshold()
            names = names.union(name_one)
        if select_k_best is True:
            name_two = self.select_k_best()
            names = names.intersection(name_two)
        if svc_select is True:
            name_three = self.svc_select()
            names = names.intersection(name_three)
        if tree_select is True:
            name_four = self.tree_select()
            names = names.intersection(name_four)

        print(names)
        return list(names)


selection = FeatureSelection(100)
selection.return_feature_set(variance_threshold=True, select_k_best=True, svc_select=False, tree_select=True)

由於使用SVC方法速度太慢，我就沒有使用它

方案二

方案二是使用遺傳演算法做特徵選擇，演算法原理我就不多闡述了,可以見我另一篇博文，雖然是用遺傳演算法解決Tsp問題，但除了編碼方式不一樣外其它幾乎差不多。

End:如有不當之處，還望不吝賜教